computer science research project

Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: CompSci Research Topics

Algorithms & data structures
Artificial intelligence ( AI )
Computer networking
Database systems
Human-computer interaction
Information security (IS)
Software engineering
Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
An evaluation of machine learning algorithms used for recommender systems in streaming services
A review of approximation algorithm approaches for solving NP-hard problems
An analysis of parallel algorithms for high-performance computing of genomic data
The influence of data structures on optimal algorithm design and performance in Fintech
A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
A comparison of streaming algorithm performance for the detection of elephant flows
A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
Exploring the feasibility of applying information theory to feature extraction in retail datasets.
Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

Applying deep learning algorithms for speech recognition in speech-impaired children
A review of the impact of artificial intelligence on decision-making processes in stock valuation
An evaluation of reinforcement learning algorithms used in the production of video games
An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
An analysis of the ethical and social implications of artificial intelligence-based automated marking
The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
An examination of the use of artificial intelligence in orthopaedic surgery
The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

An analysis of the impact of 5G technology on internet penetration in rural Tanzania
Assessing the role of software-defined networking (SDN) in modern cloud-based computing
A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
Exploring the influence of cloud computing on security risks in fintech.
An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
An evaluation of the challenges and opportunities in 6G wireless network adoption
The role of network congestion control algorithms in improving network performance on streaming platforms
An analysis of network coding-based approaches for data security
Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Free Webinar: How To Find A Dissertation Research Topic

Topics & Ideas: Database Systems

An analysis of big data management systems and technologies used in B2B marketing
The impact of NoSQL databases on data management and analysis in smart cities
An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
Exploring the role of data warehousing and business intelligence in global consultancies
An analysis of the use of graph databases for data modelling and analysis in recommendation systems
The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
An examination of the challenges and opportunities of distributed databases in supply chain management
Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
An exploration of how artificial intelligence is changing human-computer interaction patterns in children
An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
Assessing the influence of virtual and augmented reality on consumer purchasing patterns
An examination of the use of gesture-based interfaces in architecture
Exploring the impact of ease of use in wearable technology on geriatric user
Evaluating the ramifications of gamification in the Metaverse
A systematic review of user experience (UX) design advances associated with Augmented Reality
A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Research Topic Kickstarter - Need Help Finding A Research Topic?

Topics & Ideas: Information Security

A bibliometric review of current trends in cryptography for secure communication
An analysis of secure multi-party computation protocols and their applications in cloud-based computing
An investigation of the security of blockchain technology in patient health record tracking
A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
A systematic review of secure data storage solutions used for cloud computing in the fintech industry
An analysis of intrusion detection and prevention systems used in the healthcare sector
Assessing security best practices for IoT devices in political offices
An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
A comparative study of digital signature schemes adoption in property transfers
An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

A study of agile software development methodologies and their impact on project success in pharmacology
Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
A study of the impact of DevOps practices on software development and delivery in the healthcare sector
An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
An investigation of software testing techniques and methodologies for subscription-based offerings
A review of software security practices and techniques for protecting against phishing attacks from social media
An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
Exploring the impact of software development outsourcing on project success in multinational contexts
An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

An array-based optimization framework for query processing and data analytics (Chen, 2021)
Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
Embedding constructural documentation in unit tests (Nassif, 2019)
PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
A state-based approach to context modeling and computing (Yue, 2019)
A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

Using machine learning to detect and prevent cyber attacks
Developing algorithms for optimized resource allocation in cloud computing
Investigating the use of blockchain technology for secure and decentralized data storage
Developing intelligent chatbots for customer service
Investigating the effectiveness of deep learning for natural language processing
Developing algorithms for detecting and removing fake news from social media
Investigating the impact of social media on mental health
Developing algorithms for efficient image and video compression
Investigating the use of big data analytics for predictive maintenance in manufacturing
Developing algorithms for identifying and mitigating bias in machine learning models
Investigating the ethical implications of autonomous vehicles
Developing algorithms for detecting and preventing cyberbullying
Investigating the use of machine learning for personalized medicine
Developing algorithms for efficient and accurate speech recognition
Investigating the impact of social media on political polarization
Developing algorithms for sentiment analysis in social media data
Investigating the use of virtual reality in education
Developing algorithms for efficient data encryption and decryption
Investigating the impact of technology on workplace productivity
Developing algorithms for detecting and mitigating deepfakes
Investigating the use of artificial intelligence in financial trading
Developing algorithms for efficient database management
Investigating the effectiveness of online learning platforms
Developing algorithms for efficient and accurate facial recognition
Investigating the use of machine learning for predicting weather patterns
Developing algorithms for efficient and secure data transfer
Investigating the impact of technology on social skills and communication
Developing algorithms for efficient and accurate object recognition
Investigating the use of machine learning for fraud detection in finance
Developing algorithms for efficient and secure authentication systems
Investigating the impact of technology on privacy and surveillance
Developing algorithms for efficient and accurate handwriting recognition
Investigating the use of machine learning for predicting stock prices
Developing algorithms for efficient and secure biometric identification
Investigating the impact of technology on mental health and well-being
Developing algorithms for efficient and accurate language translation
Investigating the use of machine learning for personalized advertising
Developing algorithms for efficient and secure payment systems
Investigating the impact of technology on the job market and automation
Developing algorithms for efficient and accurate object tracking
Investigating the use of machine learning for predicting disease outbreaks
Developing algorithms for efficient and secure access control
Investigating the impact of technology on human behavior and decision making
Developing algorithms for efficient and accurate sound recognition
Investigating the use of machine learning for predicting customer behavior
Developing algorithms for efficient and secure data backup and recovery
Investigating the impact of technology on education and learning outcomes
Developing algorithms for efficient and accurate emotion recognition
Investigating the use of machine learning for improving healthcare outcomes
Developing algorithms for efficient and secure supply chain management
Investigating the impact of technology on cultural and societal norms
Developing algorithms for efficient and accurate gesture recognition
Investigating the use of machine learning for predicting consumer demand
Developing algorithms for efficient and secure cloud storage
Investigating the impact of technology on environmental sustainability
Developing algorithms for efficient and accurate voice recognition
Investigating the use of machine learning for improving transportation systems
Developing algorithms for efficient and secure mobile device management
Investigating the impact of technology on social inequality and access to resources
Machine learning for healthcare diagnosis and treatment
Machine Learning for Cybersecurity
Machine learning for personalized medicine
Cybersecurity threats and defense strategies
Big data analytics for business intelligence
Blockchain technology and its applications
Human-computer interaction in virtual reality environments
Artificial intelligence for autonomous vehicles
Natural language processing for chatbots
Cloud computing and its impact on the IT industry
Internet of Things (IoT) and smart homes
Robotics and automation in manufacturing
Augmented reality and its potential in education
Data mining techniques for customer relationship management
Computer vision for object recognition and tracking
Quantum computing and its applications in cryptography
Social media analytics and sentiment analysis
Recommender systems for personalized content delivery
Mobile computing and its impact on society
Bioinformatics and genomic data analysis
Deep learning for image and speech recognition
Digital signal processing and audio processing algorithms
Cloud storage and data security in the cloud
Wearable technology and its impact on healthcare
Computational linguistics for natural language understanding
Cognitive computing for decision support systems
Cyber-physical systems and their applications
Edge computing and its impact on IoT
Machine learning for fraud detection
Cryptography and its role in secure communication
Cybersecurity risks in the era of the Internet of Things
Natural language generation for automated report writing
3D printing and its impact on manufacturing
Virtual assistants and their applications in daily life
Cloud-based gaming and its impact on the gaming industry
Computer networks and their security issues
Cyber forensics and its role in criminal investigations
Machine learning for predictive maintenance in industrial settings
Augmented reality for cultural heritage preservation
Human-robot interaction and its applications
Data visualization and its impact on decision-making
Cybersecurity in financial systems and blockchain
Computer graphics and animation techniques
Biometrics and its role in secure authentication
Cloud-based e-learning platforms and their impact on education
Natural language processing for machine translation
Machine learning for predictive maintenance in healthcare
Cybersecurity and privacy issues in social media
Computer vision for medical image analysis
Natural language generation for content creation
Cybersecurity challenges in cloud computing
Human-robot collaboration in manufacturing
Data mining for predicting customer churn
Artificial intelligence for autonomous drones
Cybersecurity risks in the healthcare industry
Machine learning for speech synthesis
Edge computing for low-latency applications
Virtual reality for mental health therapy
Quantum computing and its applications in finance
Biomedical engineering and its applications
Cybersecurity in autonomous systems
Machine learning for predictive maintenance in transportation
Computer vision for object detection in autonomous driving
Augmented reality for industrial training and simulations
Cloud-based cybersecurity solutions for small businesses
Natural language processing for knowledge management
Machine learning for personalized advertising
Cybersecurity in the supply chain management
Cybersecurity risks in the energy sector
Computer vision for facial recognition
Natural language processing for social media analysis
Machine learning for sentiment analysis in customer reviews
Explainable Artificial Intelligence
Quantum Computing
Blockchain Technology
Human-Computer Interaction
Natural Language Processing
Cloud Computing
Robotics and Automation
Augmented Reality and Virtual Reality
Cyber-Physical Systems
Computational Neuroscience
Big Data Analytics
Computer Vision
Cryptography and Network Security
Internet of Things
Computer Graphics and Visualization
Artificial Intelligence for Game Design
Computational Biology
Social Network Analysis
Bioinformatics
Distributed Systems and Middleware
Information Retrieval and Data Mining
Computer Networks
Mobile Computing and Wireless Networks
Software Engineering
Database Systems
Parallel and Distributed Computing
Human-Robot Interaction
Intelligent Transportation Systems
High-Performance Computing
Cyber-Physical Security
Deep Learning
Sensor Networks
Multi-Agent Systems
Human-Centered Computing
Wearable Computing
Knowledge Representation and Reasoning
Adaptive Systems
Brain-Computer Interface
Health Informatics
Cognitive Computing
Cybersecurity and Privacy
Internet Security
Cybercrime and Digital Forensics
Cloud Security
Cryptocurrencies and Digital Payments
Machine Learning for Natural Language Generation
Cognitive Robotics
Neural Networks
Semantic Web
Image Processing
Cyber Threat Intelligence
Secure Mobile Computing
Cybersecurity Education and Training
Privacy Preserving Techniques
Cyber-Physical Systems Security
Virtualization and Containerization
Machine Learning for Computer Vision
Network Function Virtualization
Cybersecurity Risk Management
Information Security Governance
Intrusion Detection and Prevention
Biometric Authentication
Machine Learning for Predictive Maintenance
Security in Cloud-based Environments
Cybersecurity for Industrial Control Systems
Smart Grid Security
Software Defined Networking
Quantum Cryptography
Security in the Internet of Things
Natural language processing for sentiment analysis
Blockchain technology for secure data sharing
Developing efficient algorithms for big data analysis
Cybersecurity for internet of things (IoT) devices
Human-robot interaction for industrial automation
Image recognition for autonomous vehicles
Social media analytics for marketing strategy
Quantum computing for solving complex problems
Biometric authentication for secure access control
Augmented reality for education and training
Intelligent transportation systems for traffic management
Predictive modeling for financial markets
Cloud computing for scalable data storage and processing
Virtual reality for therapy and mental health treatment
Data visualization for business intelligence
Recommender systems for personalized product recommendations
Speech recognition for voice-controlled devices
Mobile computing for real-time location-based services
Neural networks for predicting user behavior
Genetic algorithms for optimization problems
Distributed computing for parallel processing
Internet of things (IoT) for smart cities
Wireless sensor networks for environmental monitoring
Cloud-based gaming for high-performance gaming
Social network analysis for identifying influencers
Autonomous systems for agriculture
Robotics for disaster response
Data mining for customer segmentation
Computer graphics for visual effects in movies and video games
Virtual assistants for personalized customer service
Natural language understanding for chatbots
3D printing for manufacturing prototypes
Artificial intelligence for stock trading
Machine learning for weather forecasting
Biomedical engineering for prosthetics and implants
Cybersecurity for financial institutions
Machine learning for energy consumption optimization
Computer vision for object tracking
Natural language processing for document summarization
Wearable technology for health and fitness monitoring
Internet of things (IoT) for home automation
Reinforcement learning for robotics control
Big data analytics for customer insights
Machine learning for supply chain optimization
Natural language processing for legal document analysis
Artificial intelligence for drug discovery
Computer vision for object recognition in robotics
Data mining for customer churn prediction
Autonomous systems for space exploration
Robotics for agriculture automation
Machine learning for predicting earthquakes
Natural language processing for sentiment analysis in customer reviews
Big data analytics for predicting natural disasters
Internet of things (IoT) for remote patient monitoring
Blockchain technology for digital identity management
Machine learning for predicting wildfire spread
Computer vision for gesture recognition
Natural language processing for automated translation
Big data analytics for fraud detection in banking
Internet of things (IoT) for smart homes
Robotics for warehouse automation
Machine learning for predicting air pollution
Natural language processing for medical record analysis
Augmented reality for architectural design
Big data analytics for predicting traffic congestion
Machine learning for predicting customer lifetime value
Developing algorithms for efficient and accurate text recognition
Natural Language Processing for Virtual Assistants
Natural Language Processing for Sentiment Analysis in Social Media
Explainable Artificial Intelligence (XAI) for Trust and Transparency
Deep Learning for Image and Video Retrieval
Edge Computing for Internet of Things (IoT) Applications
Data Science for Social Media Analytics
Cybersecurity for Critical Infrastructure Protection
Natural Language Processing for Text Classification
Quantum Computing for Optimization Problems
Machine Learning for Personalized Health Monitoring
Computer Vision for Autonomous Driving
Blockchain Technology for Supply Chain Management
Augmented Reality for Education and Training
Natural Language Processing for Sentiment Analysis
Machine Learning for Personalized Marketing
Big Data Analytics for Financial Fraud Detection
Cybersecurity for Cloud Security Assessment
Artificial Intelligence for Natural Language Understanding
Blockchain Technology for Decentralized Applications
Virtual Reality for Cultural Heritage Preservation
Natural Language Processing for Named Entity Recognition
Machine Learning for Customer Churn Prediction
Big Data Analytics for Social Network Analysis
Cybersecurity for Intrusion Detection and Prevention
Artificial Intelligence for Robotics and Automation
Blockchain Technology for Digital Identity Management
Virtual Reality for Rehabilitation and Therapy
Natural Language Processing for Text Summarization
Machine Learning for Credit Risk Assessment
Big Data Analytics for Fraud Detection in Healthcare
Cybersecurity for Internet Privacy Protection
Artificial Intelligence for Game Design and Development
Blockchain Technology for Decentralized Social Networks
Virtual Reality for Marketing and Advertising
Natural Language Processing for Opinion Mining
Machine Learning for Anomaly Detection
Big Data Analytics for Predictive Maintenance in Transportation
Cybersecurity for Network Security Management
Artificial Intelligence for Personalized News and Content Delivery
Blockchain Technology for Cryptocurrency Mining
Virtual Reality for Architectural Design and Visualization
Natural Language Processing for Machine Translation
Machine Learning for Automated Image Captioning
Big Data Analytics for Stock Market Prediction
Cybersecurity for Biometric Authentication Systems
Artificial Intelligence for Human-Robot Interaction
Blockchain Technology for Smart Grids
Virtual Reality for Sports Training and Simulation
Natural Language Processing for Question Answering Systems
Machine Learning for Sentiment Analysis in Customer Feedback
Big Data Analytics for Predictive Maintenance in Manufacturing
Cybersecurity for Cloud-Based Systems
Artificial Intelligence for Automated Journalism
Blockchain Technology for Intellectual Property Management
Virtual Reality for Therapy and Rehabilitation
Natural Language Processing for Language Generation
Machine Learning for Customer Lifetime Value Prediction
Big Data Analytics for Predictive Maintenance in Energy Systems
Cybersecurity for Secure Mobile Communication
Artificial Intelligence for Emotion Recognition
Blockchain Technology for Digital Asset Trading
Virtual Reality for Automotive Design and Visualization
Natural Language Processing for Semantic Web
Machine Learning for Fraud Detection in Financial Transactions
Big Data Analytics for Social Media Monitoring
Cybersecurity for Cloud Storage and Sharing
Artificial Intelligence for Personalized Education
Blockchain Technology for Secure Online Voting Systems
Virtual Reality for Cultural Tourism
Natural Language Processing for Chatbot Communication
Machine Learning for Medical Diagnosis and Treatment
Big Data Analytics for Environmental Monitoring and Management.
Cybersecurity for Cloud Computing Environments
Virtual Reality for Training and Simulation
Big Data Analytics for Sports Performance Analysis
Cybersecurity for Internet of Things (IoT) Devices
Artificial Intelligence for Traffic Management and Control
Blockchain Technology for Smart Contracts
Natural Language Processing for Document Summarization
Machine Learning for Image and Video Recognition
Blockchain Technology for Digital Asset Management
Virtual Reality for Entertainment and Gaming
Natural Language Processing for Opinion Mining in Online Reviews
Machine Learning for Customer Relationship Management
Big Data Analytics for Environmental Monitoring and Management
Cybersecurity for Network Traffic Analysis and Monitoring
Artificial Intelligence for Natural Language Generation
Blockchain Technology for Supply Chain Transparency and Traceability
Virtual Reality for Design and Visualization
Natural Language Processing for Speech Recognition
Machine Learning for Recommendation Systems
Big Data Analytics for Customer Segmentation and Targeting
Cybersecurity for Biometric Authentication
Artificial Intelligence for Human-Computer Interaction
Blockchain Technology for Decentralized Finance (DeFi)
Virtual Reality for Tourism and Cultural Heritage
Machine Learning for Cybersecurity Threat Detection and Prevention
Big Data Analytics for Healthcare Cost Reduction
Cybersecurity for Data Privacy and Protection
Artificial Intelligence for Autonomous Vehicles
Blockchain Technology for Cryptocurrency and Blockchain Security
Virtual Reality for Real Estate Visualization
Natural Language Processing for Question Answering
Big Data Analytics for Financial Markets Prediction
Cybersecurity for Cloud-Based Machine Learning Systems
Artificial Intelligence for Personalized Advertising
Blockchain Technology for Digital Identity Verification
Virtual Reality for Cultural and Language Learning
Natural Language Processing for Semantic Analysis
Machine Learning for Business Forecasting
Big Data Analytics for Social Media Marketing
Artificial Intelligence for Content Generation
Blockchain Technology for Smart Cities
Virtual Reality for Historical Reconstruction
Natural Language Processing for Knowledge Graph Construction
Machine Learning for Speech Synthesis
Big Data Analytics for Traffic Optimization
Artificial Intelligence for Social Robotics
Blockchain Technology for Healthcare Data Management
Virtual Reality for Disaster Preparedness and Response
Natural Language Processing for Multilingual Communication
Machine Learning for Emotion Recognition
Big Data Analytics for Human Resources Management
Cybersecurity for Mobile App Security
Artificial Intelligence for Financial Planning and Investment
Blockchain Technology for Energy Management
Virtual Reality for Cultural Preservation and Heritage.
Big Data Analytics for Healthcare Management
Cybersecurity in the Internet of Things (IoT)
Artificial Intelligence for Predictive Maintenance
Computational Biology for Drug Discovery
Virtual Reality for Mental Health Treatment
Machine Learning for Sentiment Analysis in Social Media
Human-Computer Interaction for User Experience Design
Cloud Computing for Disaster Recovery
Quantum Computing for Cryptography
Intelligent Transportation Systems for Smart Cities
Cybersecurity for Autonomous Vehicles
Artificial Intelligence for Fraud Detection in Financial Systems
Social Network Analysis for Marketing Campaigns
Cloud Computing for Video Game Streaming
Machine Learning for Speech Recognition
Augmented Reality for Architecture and Design
Natural Language Processing for Customer Service Chatbots
Machine Learning for Climate Change Prediction
Big Data Analytics for Social Sciences
Artificial Intelligence for Energy Management
Virtual Reality for Tourism and Travel
Cybersecurity for Smart Grids
Machine Learning for Image Recognition
Augmented Reality for Sports Training
Natural Language Processing for Content Creation
Cloud Computing for High-Performance Computing
Artificial Intelligence for Personalized Medicine
Virtual Reality for Architecture and Design
Augmented Reality for Product Visualization
Natural Language Processing for Language Translation
Cybersecurity for Cloud Computing
Artificial Intelligence for Supply Chain Optimization
Blockchain Technology for Digital Voting Systems
Virtual Reality for Job Training
Augmented Reality for Retail Shopping
Natural Language Processing for Sentiment Analysis in Customer Feedback
Cloud Computing for Mobile Application Development
Artificial Intelligence for Cybersecurity Threat Detection
Blockchain Technology for Intellectual Property Protection
Virtual Reality for Music Education
Machine Learning for Financial Forecasting
Augmented Reality for Medical Education
Natural Language Processing for News Summarization
Cybersecurity for Healthcare Data Protection
Artificial Intelligence for Autonomous Robots
Virtual Reality for Fitness and Health
Machine Learning for Natural Language Understanding
Augmented Reality for Museum Exhibits
Natural Language Processing for Chatbot Personality Development
Cloud Computing for Website Performance Optimization
Artificial Intelligence for E-commerce Recommendation Systems
Blockchain Technology for Supply Chain Traceability
Virtual Reality for Military Training
Augmented Reality for Advertising
Natural Language Processing for Chatbot Conversation Management
Cybersecurity for Cloud-Based Services
Artificial Intelligence for Agricultural Management
Blockchain Technology for Food Safety Assurance
Virtual Reality for Historical Reenactments
Machine Learning for Cybersecurity Incident Response.
Secure Multiparty Computation
Federated Learning
Internet of Things Security
Blockchain Scalability
Quantum Computing Algorithms
Explainable AI
Data Privacy in the Age of Big Data
Adversarial Machine Learning
Deep Reinforcement Learning
Online Learning and Streaming Algorithms
Graph Neural Networks
Automated Debugging and Fault Localization
Mobile Application Development
Software Engineering for Cloud Computing
Cryptocurrency Security
Edge Computing for Real-Time Applications
Natural Language Generation
Virtual and Augmented Reality
Computational Biology and Bioinformatics
Internet of Things Applications
Robotics and Autonomous Systems
Explainable Robotics
3D Printing and Additive Manufacturing
Distributed Systems
Parallel Computing
Data Center Networking
Data Mining and Knowledge Discovery
Information Retrieval and Search Engines
Network Security and Privacy
Cloud Computing Security
Data Analytics for Business Intelligence
Neural Networks and Deep Learning
Reinforcement Learning for Robotics
Automated Planning and Scheduling
Evolutionary Computation and Genetic Algorithms
Formal Methods for Software Engineering
Computational Complexity Theory
Bio-inspired Computing
Computer Vision for Object Recognition
Automated Reasoning and Theorem Proving
Natural Language Understanding
Machine Learning for Healthcare
Scalable Distributed Systems
Sensor Networks and Internet of Things
Smart Grids and Energy Systems
Software Testing and Verification
Web Application Security
Wireless and Mobile Networks
Computer Architecture and Hardware Design
Digital Signal Processing
Game Theory and Mechanism Design
Multi-agent Systems
Evolutionary Robotics
Quantum Machine Learning
Computational Social Science
Explainable Recommender Systems.
Artificial Intelligence and its applications
Cloud computing and its benefits
Cybersecurity threats and solutions
Internet of Things and its impact on society
Virtual and Augmented Reality and its uses
Blockchain Technology and its potential in various industries
Web Development and Design
Digital Marketing and its effectiveness
Big Data and Analytics
Software Development Life Cycle
Gaming Development and its growth
Network Administration and Maintenance
Machine Learning and its uses
Data Warehousing and Mining
Computer Architecture and Design
Computer Graphics and Animation
Quantum Computing and its potential
Data Structures and Algorithms
Computer Vision and Image Processing
Robotics and its applications
Operating Systems and its functions
Information Theory and Coding
Compiler Design and Optimization
Computer Forensics and Cyber Crime Investigation
Distributed Computing and its significance
Artificial Neural Networks and Deep Learning
Cloud Storage and Backup
Programming Languages and their significance
Computer Simulation and Modeling
Computer Networks and its types
Information Security and its types
Computer-based Training and eLearning
Medical Imaging and its uses
Social Media Analysis and its applications
Human Resource Information Systems
Computer-Aided Design and Manufacturing
Multimedia Systems and Applications
Geographic Information Systems and its uses
Computer-Assisted Language Learning
Mobile Device Management and Security
Data Compression and its types
Knowledge Management Systems
Text Mining and its uses
Cyber Warfare and its consequences
Wireless Networks and its advantages
Computer Ethics and its importance
Computational Linguistics and its applications
Autonomous Systems and Robotics
Information Visualization and its importance
Geographic Information Retrieval and Mapping
Business Intelligence and its benefits
Digital Libraries and their significance
Artificial Life and Evolutionary Computation
Computer Music and its types
Virtual Teams and Collaboration
Computer Games and Learning
Semantic Web and its applications
Electronic Commerce and its advantages
Multimedia Databases and their significance
Computer Science Education and its importance
Computer-Assisted Translation and Interpretation
Ambient Intelligence and Smart Homes
Autonomous Agents and Multi-Agent Systems.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The Top 10 Most Interesting Computer Science Research Topics

Computer science touches nearly every area of our lives. With new advancements in technology, the computer science field is constantly evolving, giving rise to new computer science research topics. These topics attempt to answer various computer science research questions and how they affect the tech industry and the larger world.

Computer science research topics can be divided into several categories, such as artificial intelligence, big data and data science, human-computer interaction, security and privacy, and software engineering. If you are a student or researcher looking for computer research paper topics. In that case, this article provides some suggestions on examples of computer science research topics and questions.

Find your bootcamp match

What makes a strong computer science research topic.

A strong computer science topic is clear, well-defined, and easy to understand. It should also reflect the research’s purpose, scope, or aim. In addition, a strong computer science research topic is devoid of abbreviations that are not generally known, though, it can include industry terms that are currently and generally accepted.

Tips for Choosing a Computer Science Research Topic

Brainstorm . Brainstorming helps you develop a few different ideas and find the best topic for you. Some core questions you should ask are, What are some open questions in computer science? What do you want to learn more about? What are some current trends in computer science?
Choose a sub-field . There are many subfields and career paths in computer science . Before choosing a research topic, ensure that you point out which aspect of computer science the research will focus on. That could be theoretical computer science, contemporary computing culture, or even distributed computing research topics.
Aim to answer a question . When you’re choosing a research topic in computer science, you should always have a question in mind that you’d like to answer. That helps you narrow down your research aim to meet specified clear goals.
Do a comprehensive literature review . When starting a research project, it is essential to have a clear idea of the topic you plan to study. That involves doing a comprehensive literature review to better understand what has been learned about your topic in the past.
Keep the topic simple and clear. The topic should reflect the scope and aim of the research it addresses. It should also be concise and free of ambiguous words. Hence, some researchers recommended that the topic be limited to five to 15 substantive words. It can take the form of a question or a declarative statement.

What’s the Difference Between a Research Topic and a Research Question?

A research topic is the subject matter that a researcher chooses to investigate. You may also refer to it as the title of a research paper. It summarizes the scope of the research and captures the researcher’s approach to the research question. Hence, it may be broad or more specific. For example, a broad topic may read, Data Protection and Blockchain, while a more specific variant can read, Potential Strategies to Privacy Issues on the Blockchain.

On the other hand, a research question is the fundamental starting point for any research project. It typically reflects various real-world problems and, sometimes, theoretical computer science challenges. As such, it must be clear, concise, and answerable.

How to Create Strong Computer Science Research Questions

To create substantial computer science research questions, one must first understand the topic at hand. Furthermore, the research question should generate new knowledge and contribute to the advancement of the field. It could be something that has not been answered before or is only partially answered. It is also essential to consider the feasibility of answering the question.

2. The Influence of Extraction Methods on Big Data Mining

Data mining has drawn the scientific community’s attention, especially with the explosive rise of big data. Many research results prove that the extraction methods used have a significant effect on the outcome of the data mining process. However, a topic like this analyzes algorithms. It suggests strategies and efficient algorithms that may help understand the challenge or lead the way to find a solution.

3. Integration of 5G with Analytics and Artificial Intelligence

According to the International Finance Corporation, 5G and AI technologies are defining emerging markets and our world. Through different technologies, this research aims to find novel ways to integrate these powerful tools to produce excellent results. Subjects like this often spark great discoveries that pioneer new levels of research and innovation. A breakthrough can influence advanced educational technology, virtual reality, metaverse, and medical imaging.

4. Leveraging Asynchronous FPGAs for Crypto Acceleration

To support the growing cryptocurrency industry, there is a need to create new ways to accelerate transaction processing. This project aims to use asynchronous Field-Programmable Gate Arrays (FPGAs) to accelerate cryptocurrency transaction processing. It explores how various distributed computing technologies can influence mining cryptocurrencies faster with FPGAs and generally enjoy faster transactions.

5. Cyber Security Future Technologies

Cyber security is a trending topic among businesses and individuals, especially as many work teams are going remote. Research like this can stretch the length and breadth of the cyber security and cloud security industries and project innovations depending on the researcher’s preferences. Another angle is to analyze existing or emerging solutions and present discoveries that can aid future research.

6. Exploring the Boundaries Between Art, Media, and Information Technology

The field of computers and media is a vast and complex one that intersects in many ways. They create images or animations using design technology like algorithmic mechanism design, design thinking, design theory, digital fabrication systems, and electronic design automation. This paper aims to define how both fields exist independently and symbiotically.

7. Evolution of Future Wireless Networks Using Cognitive Radio Networks

This research project aims to study how cognitive radio technology can drive evolution in future wireless networks. It will analyze the performance of cognitive radio-based wireless networks in different scenarios and measure its impact on spectral efficiency and network capacity. The research project will involve the development of a simulation model for studying the performance of cognitive radios in different scenarios.

8. The Role of Quantum Computing and Machine Learning in Advancing Medical Predictive Systems

In a paper titled Exploring Quantum Computing Use Cases for Healthcare , experts at IBM highlighted precision medicine and diagnostics to benefit from quantum computing. Using biomedical imaging, machine learning, computational biology, and data-intensive computing systems, researchers can create more accurate disease progression prediction, disease severity classification systems, and 3D Image reconstruction systems vital for treating chronic diseases.

9. Implementing Privacy and Security in Wireless Networks

Wireless networks are prone to attacks, and that has been a big concern for both individual users and organizations. According to the Cyber Security and Infrastructure Security Agency CISA, cyber security specialists are working to find reliable methods of securing wireless networks . This research aims to develop a secure and privacy-preserving communication framework for wireless communication and social networks.

10. Exploring the Challenges and Potentials of Biometric Systems Using Computational Techniques

Much discussion surrounds biometric systems and the potential for misuse and privacy concerns. When exploring how biometric systems can be effectively used, issues such as verification time and cost, hygiene, data bias, and cultural acceptance must be weighed. The paper may take a critical study into the various challenges using computational tools and predict possible solutions.

Computer Research Questions

Why are there so many programming languages?
Is there a better way to enhance human-computer interactions in computer-aided learning?
How safe is cloud computing, and what are some ways to enhance security?
Can computers effectively assist in the sequencing of human genes?
How valuable is SCRUM methodology in Agile software development?

Choosing the Right Computer Science Research Topic

Computer science research is a vast field, and it can be challenging to choose the right topic. There are a few things to keep in mind when making this decision. Choose a topic that you are interested in. This will make it easier to stay motivated and produce high-quality research for your computer science degree .

Select a topic that is relevant to your field of study. This will help you to develop specialized knowledge in the area. Choose a topic that has potential for future research. This will ensure that your research is relevant and up-to-date. Typically, coding bootcamps provide a framework that streamlines students’ projects to a specific field, doing their search for a creative solution more effortless.

Computer Science Research Topics FAQ

To start a computer science research project, you should look at what other content is out there. Complete a literature review to know the available findings surrounding your idea. Design your research and ensure that you have the necessary skills and resources to complete the project.

The first step to conducting computer science research is to conceptualize the idea and review existing knowledge about that subject. You will design your research and collect data through surveys or experiments. Analyze your data and build a prototype or graphical model. You will also write a report and present it to a recognized body for review and publication.

You can find computer science research jobs on the job boards of many universities. Many universities have job boards on their websites that list open positions in research and academia. Also, many Slack and GitHub channels for computer scientists provide regular updates on available projects.

There are several hot topics and questions in AI that you can build your research on. Below are some AI research questions you may consider for your research paper.

Will it be possible to build artificial emotional intelligence?
Will robots replace humans in all difficult cumbersome jobs as part of the progress of civilization?
Can artificial intelligence systems self-improve with knowledge from the Internet?

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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Research projects

Find a postgraduate research project in your area of interest by exploring the research projects that we offer in the Department of Computer Science.

We have a broad range of research projects for which we are seeking doctoral students. Browse the list of projects on this page or follow the links below to find information on doctoral training opportunities, or applying for a postgraduate research programme.

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Alternatively, if you would like to propose your own project then please include a research project proposal and the name of a possible supervisor with your application.

Available projects

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Future computing systems projects

A Multi-Tenancy FPGA Cloud Infrastructure and Runtime System
A New Generation of Terahertz Emitters: Exploiting Electron Spin
Balancing security and privacy with data usefulness and efficiency in wireless sensor networks
Blockchain-based Local Energy Markets
Cloud Computing Security
Design and Exploration of a Memristor-enabled FPGA Architecture
Design and Implementation of an FPGA-Accelerated Data Analytics Database
Designing Safe & Explainable Neural Models in NLP
Dynamic Resource Management for Intelligent Transportation System Applications
Evaluating Systems for the Augmentation of Human Cognition
Exploring Unikernel Operating Systems Running on reconfigurable Softcore Processors
Finding a way through the Fog from the Edge to the Cloud
Guaranteeing Reliability for IoT Edge Computing Systems
Hardware Aware Training for AI Systems
Hybrid Fuzzing Concurrent Software using Model Checking and Machine Learning
Job and Task Scheduling and Resource Allocation on Parallel/Distributed systems including Cloud, Edge, Fog Computing
Machine Learning with Bio-Inspired Neural Networks
Managing the data deluge for Big Data, Internet-of-Things and/or Industry 4.0 environments
Pervasive Technology for Multimodal Human Memory Augmentation
Power Management Methodologies for IoT Edge Devices
Power Transfer Methods for Inductively Coupled 3-D ICs
Problems in large graphs representing social networks
Programmable Mixed-Signal Fabric for Machine Learning Applications
Scheduling, Resource Management and Decision Making for Cloud / Fog / Edge Computing
Security and privacy in p2p electricity trading
Skyrmion-based Electronics
Skyrmionic Devices for Neuromorphic Computing
Smart Security for Smart Services in an IoT Context
Spin waves dynamics for spintronic computational devices
Technology-driven Human Memory Degradation
Ultrafast spintronics with synthetic antiferromagnets

Human centred computing projects

Advising on the Use and Misuse of Collaborative Coding Workflows
Automatic Activity Analysis, Detection and Recognition
Automatic Emotion Detection, Analysis and Recognition
Automatic Experimental Design with Human in the Loop (2025 entry onward)
Biases in Physical Activity Tracking
Computer Graphics - Material Appearance Modeling and Physically Based Rendering
Design principles for glancing at information by visually disabled users
Extending Behavioural Algorithmics as a Predictor of Type 1 Diabetes Blood Glucose Highs
Geo-location as a Predictor of Type 1 Diabetes Blood Glucose
Learning of user models in human-in-the-loop machine learning (2025 entry onward)
Machine Learning and Cognitive Modelling Applied to Video Games
Models of Bio-Sensed Body Temperature and Environment as a Refinement of Type 1 Diabetes Blood Glucose Prediction Algorithmics
Music Generation and Information Processing via Deep Learning
Understanding the role of the Web on Memory for Programming Concepts
User Modeling for Physical Activity Tracking

Artificial intelligence projects

(MRC DTP) Unlocking the research potential of unstructured patient data to improve health and treatment outcomes
Abstractive multi-document summarisation
Applying Natural Language Processing to real-world patient data to optimise cancer care
Automated Repair of Deep Neural Networks
Automatic Learning of Latent Force Models
Biologically-Plausible Continual Learning
Cognitive Robotics and Human Robot Interaction
Collaborative Probabilistic Machine Learning (2025 entry onward)
Computational Modelling of Child Language Learning
Contextualised Multimedia Information Retrieval via Representation Learning
Controlled Synthesis of Virtual Patient Populations with Multimodal Representation Learning
Data Integration & Exploration on Data Lakes
Data Lake Exploration with Modern Artificial Intelligence Techniques
Data-Science Approaches to Better Understand Multimorbidity and Treatment Outcomes in Patients with Rheumatoid Arthritis
Deep Learning for Temporal Information Processing
Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
Event Coreference at Document Level
Explainable and Interpretable Machine Learning
Formal Verification for Robot Swams and Wireless Sensor Networks
Formal Verification of Robot Teams or Human Robot Interaction
Foundations and Advancement of Subontology Generation for Clinically Relevant Information
Generating Goals from Responsibilities for Long Term Autonomy
Generating explainable answers to fact verification questions
Integrated text and table mining
Knowledge Graph Construction via Learning and Reasoning
Knowledge Graph for Guidance and Explainability in Machine Learning
Machine Learning for Vision and Language Understanding
Multi-task Learning and Applications
Neuro-sybolic theorem proving
Ontology Informed Machine Learning for Computer Vision
Optimization and verification of systems modelled using neural networks
Probabilistic modelling and Bayesian machine learning (2025 entry onward)
Representation Learning and Its Applications
Software verification with contrained Horn clauses and first-order theorem provers
Solving PDEs via Deep Neural Nets: Underpinning Accelerated Cardiovascular Flow Modelling with Learning Theory
Solving mathematical problems using automated theorem provers
Solving non-linear constraints over continuous functions
Symmetries and Automated Theorem Proving
Text Analytics and Blog/Forum Analysis
Theorem Proving for Temporal Logics
Trustworthy Multi-source Learning (2025 entry onward)
Verification Based Model Extraction Attack and Defence for Deep Neural Networks
Zero-Shot Learning and Applications

Software and e-infrastructure projects

Automatic Detection and Repair of Software Vulnerabilities in Unmanned Aerial Vehicles
Combining Concolic Testing with Machine Learning to Find Software Vulnerabilities in the Internet of Things
Component-based Software Development.
Effective Teaching of Programming: A Detailed Investigation
Exploiting Software Vulnerabilities at Large Scale
Finding Vulnerabilities in IoT Software using Fuzzing, Symbolic Execution and Abstract Interpretation
Using Program Synthesis for Program Repair in IoT Security
Verifying Cyber-attacks in CUDA Deep Neural Networks for Self-Driving Cars

Theory and foundations projects

Application Level Verification of Solidity Smart Contracts
Categorical proof theory
Formal Methods: Hybrid Event-B and Rodin
Formal Methods: Mechanically Checking the Semantics of Hybrid Event-B
Formal Semantics of the Perfect Language
Mathematical models for concurrent systems

James Elson projects

Data science projects.

Data Wrangling
Fishing in the Data Lake
Specifying and Optimising Data Wrangling Tasks

Sophia Ananiadou projects

Mauricio alvarez projects, richard banach projects, riza batista-navarro projects, ke chen projects, sarah clinch projects, angelo cangelosi projects, jiaoyan chen projects, lucas cordeiro projects, louise dennis projects, clare dixon projects, suzanne embury projects, marie farrell projects, alejandro frangi projects, andre freitas projects, michael fisher projects, gareth henshall projects, simon harper projects, caroline jay projects, samuel kaski projects, dirk koch projects, konstantin korovin projects, kung-kiu lau projects, zahra montazeri projects, christoforos moutafis projects, tingting mu projects, anirbit mukherjee projects, mustafa mustafa projects, goran nenadic projects, paul nutter projects, nhung nguyen projects, pierre olivier projects, norman paton projects, vasilis pavlidis projects, pavlos petoumenos projects, steve pettifer projects, oliver rhodes projects, giles reger projects, rizos sakellariou projects, uli sattler projects, andrea schalk projects, renate schmidt projects, robert stevens projects, sandra sampaio projects, viktor schlegel projects, youcheng sun projects, tom thomson projects, junichi tsujii projects, markel vigo projects, ning zhang projects, liping zhao projects.

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Computer Science Projects

Computer science is a popular topic of study today, with numerous applications spanning a wide range. Final-year students frequently find it difficult to select the appropriate computer science project. On the final day of graduation, projects are the only thing that matters. Any IT-related industry where projects have a substantial impact can be chosen for a job or further education. Project work indicates knowledge depth as well as some soft skills like creativity and problem-solving. Your interview prospects will also improve as a result of your final year projects. As a result, in their last year of graduation, students are required to complete a project.

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Recent Project Articles !

C++ Projects
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Python Projects
Project Ideas
Department Store Management System(DSMS) using C++
Test Cases For Signup Page Using C Language
Shopping Cart Project Using C Language
OpenCV C++ Program for Face Detection
OpenCV C++ Program for coin detection
OpenCV C++ Program to blur an image
OpenCV C++ Program to create a single colored blank image
OpenCV C++ Program to blur a Video
OpenCV C++ Program to play a video
Creating a PortScanner in C
Student Data Management in C++
OpenGL program for Simple Ball Game
Implementation of Minesweeper Game
Finding cabs nearby using Great Circle Distance formula
Program to remotely Power On a PC over the internet using the Wake-on-LAN protocol.

Java Projects :

A Group chat application in Java
Generating Password and OTP in Java
Creative Programming In Processing | Set 1 (Random Walker)
Creative Programming In Processing | Set 2 (Lorenz Attractor)

Python Projects :

Make Notepad using Tkinter
Color game using Tkinter in Python
Python | Message Encode-Decode using Tkinter
XML parsing in Python
Desktop Notifier in Python
Hangman Game in Python
Junk File Organizer in Python
Browser Automation Using Selenium
Tracking bird migration using Python-3
Twitter Sentiment Analysis using Python
Image Classifier using CNN
Implementing Photomosaics
Working with Images in Python
OpenCV Python Program to blur an image
Opencv Python program for Face Detection
Cartooning an Image using OpenCV – Python
OpenCV Python Program to analyze an image using Histogram
OpenCV Python program for Vehicle detection in a Video frame
DNA to Protein in Python 3
Viruses – From Newbie to pro
Handling Ajax request in Django
Working with zip files in Python
Morse Code Translator In Python
Simple Chat Room using Python
Creating a Proxy Webserver in Python | Set 1
Creating a Proxy Webserver in Python | Set 2
Project Idea | Audio to Sign Language Translator
Understanding Code Reuse and Modularity in Python 3
Multi-Messenger : A python project, messaging via Terminal
Movie recommendation based on emotion in Python
Implementing Web Scraping in Python with BeautifulSoup
Computer Vision module application for finding a target in a live camera

Web Development Projects

Design an Event Webpage using HTML & CSS
Design a Parallax Webpage using HTML & CSS
Design a Webpage like Technical Documentation using HTML & CSS
Design Homepages like Facebook using HTML and CSS
Page for online food delivery system using HTML and CSS
Responsive sliding login and registration forms using HTML CSS and JavaScript?
Design a Student Grade Calculator using JavaScript
Slide Down a Navigation Bar on Scroll using HTML, CSS, and JavaScript
Design a BMI Calculator using JavaScript
Task Tracker Project

Project Ideas :

Project Idea | (Static Code Checker for C++)
Project Idea | (Dynamic Hand Gesture Recognition using neural network)
Project Idea | God’s Eye
Project Idea | (Ca-solutions)
Project Idea | College Connect
Project Idea | Empower Illiterate
Project Idea | (Remote Lab Assistance)
Project Idea | (Project Approval System)
Project Idea | (Online Course Registration)
Project Idea | (Universal Database Viewer)
Project Idea | Sun Rise/Set Time Finder
Project Idea | Automatic Youtube Playlist Downloader
Project Idea | Aadhaar Thumb: A Platform to All Services
Project Idea | (Health services & Medical outcome monitoring)
Project Idea| (Magical Hangouts: An Android Messaging App)
Project Idea | JamFree
Project Idea | AI Therapist
Project Idea | Get Your Logo
Project Idea | ( Client Master)
Project Idea | (A Game of Anagrams )
Project Idea | Breakout game in Python
Project Idea | (Games using Hand Gestures)
Project Idea | Amanda: A Smart Enquiry Chatbot
Project Idea | (A.T.L.A.S: App Time Limit Alerting System)
Project Idea | Sign Language Translator for Speech-Impaired
Project Idea | Personality Analysis using hashtags from tweets
Project Idea | Recommendation System based on Graph Database
Creating a C/C++ Code Formatting tool with help of Clang tools
Project Idea (Augmented Reality – QR Code Scanner)
Project Idea (Augmented Reality – ARuco Code Detection and Estimation)
Project Idea | (CSE Webnode)
Project Idea | College Network
Project Idea | (Online UML Designing Tool)
Project Idea | Voice Based Email for Visually Challenged
Project Idea | Assist Bot
Project Idea | Social-Cop
Project Idea | MediTrack
Project Idea | (CAPTURED)
Project Idea | LinkBook
Project Idea | (Trip Planner)
Project Idea | EveMythra Bot
Project Idea | Green Rides
Project Idea | E-Ration Shop
Project Idea | Smart Elevator
Project Idea | Get Me Through
Project Idea | Innovate Email
Project Idea | NextVAC Platform
Project Idea | League of Fitness
Project Idea | (A Personal Assistant)
Project Idea | (Smart Restaurants)
Project | Scikit-learn – Whisky Clustering
Creating a Calculator for Android devices
Project Idea | Airport Security Using Beacon
Project Experience | (Brain Computer Interface)
Project Idea | ( True Random Number Generator)
Project Idea | Distributed Downloading System
Project Idea | (Personalized real-time update system)
Project Idea | Attendance System Using Smart Card
Project Idea | (Detection of Malicious Network activity)
Project Idea | Smart Waste Management System
Project Idea – Bio-Hashing : Two factor authentication
Project Idea | noteSort (Classify handwritten notes)
Project Idea | Health Application powered by IBM Watson
Project Idea | Collaborative Editor Framework in Real Time
Project Idea | Department Data Analysis Mobile Application
Project Idea | Analysis of Emergency 911 calls using Association Rule Mining
Crop monitoring and smart farming using IoT
MyHelper (Access your phone from anywhere without Internet)
Project Idea | (Robust Pedestrian detection)
Project Idea | ( Character Recognition from Image )
Project Idea | (Model based Image Compression of Medical Images)
Project Idea | Motion detection using Background Subtraction Techniques
Project Idea | (Optimization of Object-Based Image Analysis with Super-Pixel for Land Cover Mapping)
A Number Link Game
Designing Use Cases for a Project
Building a Basic Chrome Extension
How to write a good SRS for your Project
Creating WYSIWYG Document Editor | Natural Language Programming

Computer Science – FAQs

1. what is computer science .

Computer science (CS) is the study of computers and algorithmic processes including their principles, their hardware and software designs, their applications, and their impact on society.

2. Which is the best project in the final year?

The best final-year project is subjective and depends on your interests and skills. Choose a project that appeals to your interests, challenges you, and provides real learning possibilities.

3. How do I choose a major project for CSE?

To choose a major project for Computer Science Engineering (CSE), follow these steps: Identify your interests and strengths within CSE. Research current trends and emerging technologies in the field. Discuss project ideas with professors, peers, and industry professionals. Consider the project’s feasibility, scope, and potential impact. Select a project that excites you and aligns with your academic goals.

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BS | Research Opportunities

Main navigation.

The Computer Science Department at Stanford have faculty and students that are globally recognized for their innovative and cutting-edge research. We offer scholars various opportunities at their disposal to participate in undergraduate research. If you are interested in research, we welcome you to explore the opportunities at your disposal.

CURIS Research

The program for CS undergrad Summer research. Participating students will work on their projects full-time and are paid a stipend for living expenses.

Independent Study

Undergraduate research is often done through CURIS, for academic credit, or through an informal arrangement with a professor.

Getting Started

Undergraduate CS research website . The most reliable way to learn about projects you can get involved in is through the undergraduate CS research website. Throughout the year, professors have openings for undergrads to do work in their labs. They post descriptions of these projects on the site for your perusal. This site lists CS research projects during the academic year for course credit, CS research projects for the Summer quarter under CURIS (paid internship), and research projects in other departments that include CS applications.
Go to office hours . Find a professor whose research interests you want to learn more about. Discuss what possibilities are available or find out more about a particular group. Often the professor will be able to direct you to some research papers that might be valuable to read or other groups that you might find interesting. It's always a good idea to email a professor and let them know that you will be coming in. That way if their office hours are particularly busy, they can suggest another time.
Connect with a graduate student . Graduate students work on projects every day and deal with most of the details, they are probably one of the best sources of information. They will have a good idea of what role you could initially play in the project and will also be able to give an honest assessment of what it is like to work with the professor and what are the expectations of the group. Finally, if you decide to work with the group, the graduate students will probably be the ones who will be mentoring you in the day-to-day aspects of your work. Before you choose a project, try to meet with at least one graduate student in the group, preferably one that would be mentoring you. If you are still deciding between projects, ask the graduate students for their opinion.
Read your email . The bscs list is constantly getting announcements about presentations that are being given by faculty, advanced graduate students, and visiting faculty. Take the time to read through some of the abstracts and pick a few that interest you. These announcements are not usually forwarded to the considering_cs list. If you are interested in getting these announcements, visit the course advisor and declare CS !
CURIS poster sessions . At the end of the Summer quarter and the beginning of the Fall quarter, the CURIS program organizes poster sessions for undergraduates to present their Summer research projects. This is a great opportunity for you to get first-hand information about your peers' research experience as well as potential project ideas and research groups of interest. In addition, the display in the Gates lobby shows a collection of both undergraduate and graduate research projects year-round.
500 level seminars . All of the CS 500 level courses are topic seminars. For instance, CS 547 Seminar focuses on Human-Computer Interaction topics. Each week, a different speaker comes in and presents their research. Sometimes the speakers are Stanford professors, graduate students, or they're outside visitors. The presentations are technical, check the schedules on the class web pages to find talks that may be interesting.
CS300 ( speaker schedule ) . At the beginning of each academic year, all new PhD students are required to take CS 300. In each seminar, two professors come in and describe their research work. The idea is to give PhD students an overview of the ongoing research so they can decide which groups they would like to join. Although the class is technically for PhD students, undergraduate and Master's students can enroll. The presentations are likely to be somewhat technical, but since they are geared toward PhD students with a broad variety of interests, they should be fairly accessible.

Featured Research Projects

Active logic.

Through employing metareasoning, Active Logic is a flexible alternative to more traditional Artificial Intelligence systems. Because Active Logic is an application that tracks its reasoning, it is able to reason about its own inferences, and thereby recognize and recover from errors.

Baxter Robots are Learning to Think, See, and Cook

Baxters are low cost humanoid robots meant for adaptable manufacturing purposes by using long nimble arms and a suite of visual and tactile sen- sors. At the University of Maryland, re- searchers are exploring computer vision, machine learning, and artificial intelligence by training the Baxters to pour water into a moving jar, learn to cook by watching YouTube, and work with other robots.

CommentIQ: Would Your Comment Make The New York Times?

DSU: Dynamic Software Updating

Dynamic Software Updating (DSU) acknowledges that software systems are imperfect and works to correct this inconvenience by permitting programs to be updated while they run. Requiring no redundant hardware, this software has been tested through theoretical programming language development and practical implementation.

EbN: Encounter-based Networking

Mobile social applications provide new information sharing and networking opportunities based on a userâ€™s location, activity, and set of nearby users. The underlying communication protocols for these applications must be carefully designed to not leak sensitive information â€”identity, movement patterns, etc.â€” to strangers without prior consent. EbN is a communication system which meets the needs of a wide range of mobile social apps while providing strong security guarantees.

Gas Station Project

The Gas Station Project is an approximation algorithm that can assess routing problems and generalize the shortest distances to reach a destination. Along with "The Traveling Salesman Problem", this research is based off of a new cost model dependent on gas prices rather than distance traveled.

Genetic study of children from low-income countries may reveal reasons for deadly cases of diarrhea

Moderate to severe diarrhea continues to be a deadly disease, especially for children in low-income countries. Meanwhile, the pathogens causing the condition are still not entirely known. Teams of researchers are using new biological and analytical techniques to look for previously unknown sources of the illness.

Guiding Hidden Layer Representations for Improved Rule Extraction from Neural Networks

Artificial Neural Networks (ANNs) are flexible learning algorithms that are used to solve problems from speech synthesis to handwriting recognition or even self-driving cars. But how ANNs generate outputs can appear to be like a black box, which limits some areas of use of ANNs. Former PhD student Thuan Huynh and his mentor Jim Reggia have developed an approach to allow us to peer inside the black box, leading to more impactful applications of ANNs.

LCCD: Laboratory for Computational Cultural Dynamics

The Laboratory for Computational Cultural Dynamics (LCCD) is a multidisciplinary research laboratory partnered with the UM Institute for Advanced Computer Studies (UMIACS). The lab focuses on the development of algorithms to automatically track open source information related to terror groups, tribes, and socio-cultural-political entities, as well as conduct behavioral analytics (including forecasting, what-if reasoning and policy formulation).

NewsStand: An Interactive News Organizer

Nowadays, there is a great amount of information available and generally it is difficult to locate a website that contains local news from a city, or even a small village that you are interested in. As an example, suppose that someone is going to move to a new area and he is interested in reading the local news to find out if it is a good neighborhood. An effective way is to search for news websites that are localized for the city of interest and look for articles that are focused on the city.

Persona is an improvement to online social networks (OSNs) that hides user data with attribute-based encryption (ABE). This feature gives users, rather than the OSN itself, the ability to define policy over who has access to their private data.

Saliency-Assisted Navigation of Very Large Landscape Images

Imagine a group of hikers lost in a park with no means of communication or signaling. Traditional search and rescue techniques require many man-hours and often turn up nothing. So the ability to detect the presence of interesting objects within a current highresolution image of the park would be highly desirable in such a scenario.

Scaling Computation of Graph Structured Data with NScale

In this day and age, the already vast amounts of data being generated that we have to deal with are still increasing in size by the second. The ”Big Data” buzzword keeps becoming more relevant not only in computer science but in nearly all sciences, and with good reason. The more data in a specific domain increases in size the more valuable it is considered. There may exist incredibly useful insight in the data that remains untapped until analyzed. Researchers in the field of Database Systems have been on the hunt for a fast, efficient, and scalable way we can analyze very large volumes of data.

Social Street View: Blending Immersive Street Views with Geo-tagged Social Media

This project presents an immersive geo-spatial social media system for virtual and augmented reality environments. With the rapid growth of photo-sharing social media sites such as Flickr, Pinterest, and Instagram, geo-tagged photographs are now ubiquitous. However, the current systems for their navigation are unsatisfyingly one- or two-dimensional. In this paper, we present our prototype system, Social Street View, which renders the geo-tagged social media in its natural geo-spatial context provided by immersive maps, such as Google Street View.

State-of-the-Art in Automated Graphical User Interface Testing

People rely on and use software for many tasks, ranging from launching spacecraft, writing reports, and communicating with other people. But, because of bugs, people are not always happy with software. Sometimes, software does not do what it should and this can make users unhappy or sometimes have very serious effects on the world. Software should be a tool that people can rely on, but often it is not. The (in)famous blue screen of death came to symbolize software errors.

tssnet: lightweight network simulation

Timestep stochastic simulation can evaluate the performance of computer networks at a cost several orders less than packet-level simulation (the current de facto method). Unlike packet-level simulation, the network state is updated probabilistically at steps of time. This results in fast lightweight simulators, especially for high-speed networks.

What If Our Clothes Could Show How Fast We Run?

We live in a connected world. As of 2014, 72% of American adults have a profile on at least one social networking site. Performance feedback devices, including the Garmin GPS watch, fitbit, and Nike+, coupled with exercise based social networks, such as MapMyRun and Strava, have created a growing online social community for runners and cyclists; however, the exercise itself is still performed individually. When compared to individual exercise, exercising as a group has been shown to provide a number of benefits including increased enjoyment and intensity.

2020-2021 SURE Research Projects in CSE

This page lists summer research opportunities in CSE that are available through the SURE Program. To learn more or apply, visit: https://sure.engin.umich.edu/ .

Please carefully consider each of the following projects, listed below, before applying to the SURE Program.
You must indicate your top three project choices on your SURE application, in order of preference, using the associated CSE project number.
Questions regarding specific projects can be directed to the listed faculty mentor.

Project descriptions

CSE Project #1: Natural Language Processing for Understanding Media Bias and Fake News Faculty Mentor: Lu Wang [wangluxy @ umich.edu] Prerequisites: EECS 445 (Machine Learning), probability and statistics, experience with natural language processing problems, proficient in Python. Description: News media play a vast role not just in supplying information, but in selecting, crafting, and biasing that information to achieve both nonpartisan and partisan goals. We aim to automate media bias detection from news articles, and quantify and further highlight biased content in order to promote the transparency of news production as well as enhance readers’ awareness of media bias. This project will explore and design natural language processing and machine learning algorithms to detect media bias. Specifically, we will work on developing information extraction systems, e.g., important entities and narrative structure will be extracted automatically from news articles. The developed tools will also be used for understanding fake news. Expected research delivery mode: Hybrid

CSE Project #2: Computational Strategic Reasoning Faculty Mentor: Michael Wellman [wellman @ umich.edu] Prerequisites: Programming ability; interest/background in finance, economics, game theory, and/or statistics (helpful though not required). Description: The Strategic Reasoning Group (strategicreasoning.org) develops computational tools to support reasoning about complex strategic environments. Recent applications include scenarios arising in finance and cyber-security. We employ techniques from agent-based modeling, game theory, and machine learning. Expected research delivery mode: Too soon to say

CSE Project #3: Taming the Performance Bottlenecks of Modern Web Applications Faculty Mentor: Baris Kasikci [barisk @ umich.edu] Prerequisites: EECS 482 Description: Modern data-center applications suffer significant slow-down due to large number instruction cache-misses. To reduce such cache-misses, recent studies have advocated the introduction of a new code prefetch instruction. While warehouse-scale processors do not support this feature yet, some mobile processors already support this code prefetch instruction. In this study, we will design a compiler backend to inject code prefetch instruction both statically and based on profile data in order to evaluate several data-center applications on mobile such processors. Expected research delivery mode: Too soon to say

CSE Project #4: Web automation using program synthesis Faculty Mentor: Xinyu Wang [xwangsd @ umich.edu] Prerequisites: EECS 485 or equivalent, and familiarity with HTML/DOM/JS Description: Many computer end-users often need to perform tasks that involve the web, such as filling online forms, extracting data, which are repetitive and tedious in nature. On the other hand, there are existing programming languages that can be used to automate these tasks. However, writing web automation scripts is far beyond the capability of end-users who have very little programming background. In this project, we aim to help users automate web-related programming tasks using program synthesis. Expected research delivery mode: Too soon to say

CSE Project #5: Interactive program synthesis Faculty Mentor: Xinyu Wang [xwangsd @ umich.edu] Prerequisites: Familiarity with one programming language. Description: Program synthesis aims to automatically generate programs from user intent expressed in some high-level format (such as input-output examples). It has found a lot of applications, for instance, in data science, software development, etc. While there has been a lot of algorithmic advancements in program synthesis techniques, it is still unclear what is the best way for synthesizers to interact with users. In this project, we will explore how to design interactive program synthesis algorithms as well as good user interfaces for these techniques. Expected research delivery mode: Too soon to say

CSE Project #6: Superoptimization using program synthesis Faculty Mentor: Xinyu Wang [xwangsd @ umich.edu] Prerequisites: Compilers, strong programming and engineering background. Description: The goal of superoptimization is to automatically derive compiler optimizations. It automatically searches among a space of optimizations and apply those that can be applied for the input program. The advantage of superoptimization is that it can dramatically reduce human effort and at the same time potentially generate better optimizations. In this project, we will look at how to use program synthesis and program analysis to automatically derive better optimizations more efficiently, compared to prior superoptimization techniques. Expected research delivery mode: Too soon to say

CSE Project #7: Censored Planet: A Global Observatory for Internet Censorship Faculty Mentor: Roya Ensafi [ensafi @ umich.edu] Prerequisites: EECS 388 and EECS 482 Description: The Internet Freedom community’s understanding of the current state and global scope of censorship remains limited: most work to-date has focused on the practices of particular networks and countries, or on the reachability of small sets of online services and from a small number of volunteers. Creating a global, data-driven view of censorship is a challenging proposition, since censorship practices are intentionally opaque, and there are a host of mechanisms and locations where disruptions can occur. Moreover, the behavior of the network can vary depending on who is requesting content from which location.

Fall 2018, Prof. Ensafi launched a pilot of Censored Planet, an online observatory for Internet censorship that applies all of next-generation measurement techniques in order to rapidly, continuously, and globally track online censorship. Data from the pilot has already been used by dozens of organizations, and it has helped provide insight into important events like Saudi Arabia’s reaction to the death of Jamal Khashoggi, the proliferation of DPI-based censorship products, and recent HTTPS interception attacks sponsored by the government of Kazakhstan.

We seek to extend and fully operationalize Censored Planet and make data from next-generation remote censorship measurements more useful to the entire Internet Freedom community. We plan to mature the project from a pilot to a production system with significant improvements in performance, stability, usability, and code quality; implement an API and new “rapid focus” capabilities to agily respond to world events; and develop aggregation and analysis tools to automatically extract useful insights from that data. We will also cultivate a community of civil society organizations and tool developers to ensure the data best serves real-world needs.

By helping create a more complete picture of global censorship than ever before, Censored Planet will allow researchers and policymakers to closely monitor for deployment of censorship technologies, track policy changes in censoring nations, and better understand the targets of interference. Making opaque censorship practices more transparent at a global scale will help counter the proliferation of these growing restrictions to online freedom. Expected research delivery mode: Remote

CSE Project #8: Supporting K-5 Children Learning While Using the Collabrify Roadmap Platform Faculty Mentor: Elliot Soloway [soloway @ umich.edu] Prerequisites: Competency in Javascript, databases, interfaces. Description: The Center for Digital Curricula in the College of Engineering provides deeply-digital curricula, standards-aligned to K-5 classrooms – free. During the fall 2020 semester, over 5,000 K-5 students are using the Center’s curricula on a daily basis. Students use the Collabrify Roadmap Platform to enact the digital curricula. Teachers and students request changes to the Platform; and researchers see opportunities to make the Platform still more effective. During the summer, then, the Center is seeking two ugrads to work on projects to implement the requested changes to the Platform. Join us in helping children to learn more effectively! Expected research delivery mode: Hybrid

CSE Project #9: Computer Vision for Physical and Functional Understanding Faculty Mentor: David Fouhey [fouhey @ umich.edu] Prerequisites: Good grades in EECS 442 OR EECS 445. Description: The lab is broadly focused on building 3D representations of the world and understanding human/object interaction. Potential projects include learning about: navigating environments, object articulations, commonsense physical properties of objects, and hand grasps. Please look at:http://web.eecs.umich.edu/~fouhey/ for a sense of what projects we’ve done in the past. We will find a specific project based on mutual interest and particular abilities (e.g., stronger systems programming abilities, experience with graphics, etc.). Students looking for a longer term project continuing during the school year are strongly encouraged to apply. Expected research delivery mode: Too soon to say

CSE Project #10: Does Wealth Matter? Learning Generative Models with Prediction Markets Faculty Mentor: Mithun Chakraborty and Sindhu Kutty [skutty @ umich.edu] Prerequisites: EECS 445 and STATS 412 (or equivalents) preferred. Description: As recent events have highlighted, polling can be messy, misleading and prone to misinterpretation. Markets have the advantage over polls in having built-in financial incentives and timely responses, and have been empirically observed to outperform alternative forecasting tools such as polls. However, when traders have varying degrees of wealth, are markets egalitarian? Moreover, how precise are they and what factors impact their precision? We will answer these questions in the context of Prediction Markets by tying market prices to learning a generative model of the outcome space. We will also explore other connections between convergence in Machine Learning algorithms (especially Bayesian processes) and equilibria in these markets.

Prediction markets (e.g. Iowa Electronic Markets, PredictIt, etc.) are a type of financial market the purpose of which is to elicit the personal beliefs of traders about a future uncertain event and aggregate these beliefs into the market price. In this project, students will implement and execute a set of experiments on the interaction of a new prediction market design with simulated trading agents having diverse risk attitudes and help address the above research questions in different environments in a systematic manner. An understanding of connections to Machine Learning algorithms would be illustrative for gauging the accuracy, and hence reliability, of Prediction Markets and can, in turn, inform innovations in their design. The learning outcome for students will be hands-on experience in interdisciplinary research with connections to Machine Learning and Computational Economics. Expected research delivery mode: Remote

CSE Project #11: Hazel Notebooks: Building a Better Jupyter Faculty Mentor: Cyrus Omar [comar @ umich.edu] Prerequisites: EECS 490 or equivalent is preferred, but not required. Description: The popular Jupyter lab notebook environment is powerful, but it has a problem: results stored in a notebook are not reproducible, because the user can execute cells out of order. In our group, we are developing a new live functional programming environment called Hazel (hazel.org). Right now, Hazel does not support multiple program cells. This project will turn Hazel into a next-generation version of Jupyter by adding support for notebooks with multiple cells, with dependencies between them. We will solve the reproducibility problem by developing a mechanism conjectured in a recent paper in our group: fill-and-resume. Expected research delivery mode: Too soon to say

CSE Project #12: Hazel: A Live Functional Programming Environment Faculty Mentor: Cyrus Omar [comar @ umich.edu] Prerequisites: EECS 490 or equivalent is preferred, but not required. Description: Hazel (hazel.org) is a live functional programming environment that is able to typecheck, transform and even execute incomplete programs, i.e. programs with holes. There are a number of projects available within the Hazel project for a student interested in research into programming languages. Expected research delivery mode: Too soon to say

CSE Project #13: Ubiquitous Health Sensing Faculty Mentor: Alanson Sample [apsample @ umich.edu] Prerequisites: Experience with embedded systems, computer vision, or machine learning Description: Effective means of unobtrusive and continuous monitoring of one’s health could transform how we detect and treat illnesses. This project aims to create a long-range health monitoring system that can passively measure an individual’s vital signs and daily activities from a distance of up to three meters. Building off of novel sensing techniques developed in the Interactive Sensing and Computing Lab, SURE students will work with faculty and graduate student mentors to create a fully working end-to-end system, utilizing embedded systems, computer vision, and machine learning. Expected research delivery mode: Hybrid

CSE Project #14: The Internet of Everything: Bringing everyday objects into the digital world with RFID tags Faculty Mentor: Alanson Sample [apsample @ umich.edu] Prerequisites: Strong programming skills. Description: RFID tags are battery-free, paper-thin stickers that can communicate with RFID readers from +8 meters of distance. These tags offer a minimalistic means of instrumenting everyday objects. By monitoring changes in the low-level communication channel parameters between the tag and reader, it is possible to turn an RFID tag into an ultra-low-cost, battery-free sensor. Applications include in-home activity inferencing, interactive physical objects, and health and wellness monitoring. Expected research delivery mode: Too soon to say

CSE Project #15: Computer Vision for Physical and Functional Understanding Faculty Mentor: Alanson Sample [apsample @ umich.edu] Prerequisites: Preferred EECS 311 or EECS 373. Description: This project encompasses a number of efforts at developing energy harvesting, battery free sensing systems that can be easily embedded into everyday objects and thus allowing for near perpetual operation. Topics include ambient energy harvesting techniques, platform architecture and power management, and debugging tools that deal with intermittent power. Expected research delivery mode: Too soon to say

CSE Project #16: Adversarial Human-AI Interactions in the On-Demand Economy Faculty Mentor: Nikola Banovic [nbanovic @ umich.edu] Prerequisites: Familiarity with programming (i.e., Python), interest in applied machine learning and human-computer interaction. Description: AI has started to transform the nature of work in many sectors of the economy. One of the most tangible transformations has been in the on-demand economy, for services such as grocery delivery, ride-hailing, and other last-mile services, where its advances have allowed a shift towards greater efficiency, through the use of AI-mediated platforms. On-demand work, with its promises of flexibility, independence and entrepreneurship is also an attractive option for individuals seeking a low-barrier entry into employment and economic opportunities. However, several recent debates around the employment status of workers with services such as Uber, Lyft and Instacart have shined a light on the adversarial relationships between workers and platforms, and the negative effects of opaque algorithms on workers’ well-being. In this project, we seek to design computational methods to audit these opaque platforms to uncover sources of adversarial human-AI interactions that may be potentially harmful to on-demand workers. Our goal is to understand the design of algorithmic platforms that enhance worker well-being and their access to economic opportunities. Expected research delivery mode: Remote

CSE Project #17: Novel Architectures to Compute with Graphs Faculty Mentor: Valeria Bertacco [valeria @ umich.edu] Prerequisites: EECS 281, EECS 370. Recommended: C++, scripting. Description: More and more applications rely on graphs as the underlying data structure: from social networks, to internet’s web connections, to geo maps, to ML algorithms and even consumers’ product preferences. The performance of these algorithms is often limited by the latency of accessing vertices in memory, whose access present poor spatial locality. The goal of this project is to boost the performance of graph-based algorithms by developing hardware and software solutions to this end: we plan to work on the data layout, on ad-hoc data structures and on designing dedicated hardware acceleration blocks. We hope to boost the performance of graph traversals by 3-5x. Expected research delivery mode: Too soon to say

CSE Project #18: From High-Level Language to Hardware — Without the Hardware Design Faculty Mentor: Valeria Bertacco [valeria @ umich.edu] Prerequisites: EECS 281. Recommended: C++, scripting. Description: This project explores a new hardware design flow, where the starting point is an application specified in a domain-specific language (more specialized than C) like Halide or GraphIt, and the endpoint is a hardware system equipped with specialized hardware accelerators, so to execute the application much faster than it would be possible in software. To reach the endpoint, we will work on the back-end of the compiler, so to target the primitives available in the hardware accelerators. Expected research delivery mode: Too soon to say

CSE Project #19: Computing on Encrypted Data Faculty Mentor: Valeria Bertacco [valeria @ umich.edu] Prerequisites: EECS 280, EECS 370. Recommended: C++, scripting. Description: In the age of big data, privacy is a key concern in sharing data. Unfortunately, the field of security is riddled with stories of security attacks…even to the most secure enclaves. The solution we want to investigate with this project uses encryption technology to encrypt data locally, transfer it to the cloud for any required computation, and receive encrypted results back. The enhanced cloud system performs the computation directly on the encrypted data without an access key — it never accesses the plaintext data nor can it decrypt the sensitive data. Only the end device, can decrypt the result and store it locally. Expected research delivery mode: Too soon to say

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Research projects.

The CS department has resources available for research projects. The following is a quick start guide to research projects in Computer Science Department. This quick start guide does not cover all topics and it is recommended that you consult the CS Guide for more information.

This guide is designed to help those beginning a research project by pointing to appropriate sections of the CS Guide for typical start-up tasks. Research projects typically need the following: storage space that can be shared by members of the research group, a web presence (possibly driven by a back-end database), mailing lists, code/document repositories. Here is how each of these are implemented and requested in the Computer Science Department.

Project Disk Space - We encourage projects (even single-person projects) to use disk space outside of the user home directory filesystem. This has several benefits. First, the quota is separate from any particular project member and can be much larger than we allow for home directories. Second, project members can be added and removed to change access without moving the files themselves. Third, users can collaborate and share files without having to give others access to their home directory. Finally, by keeping projects in separate partitions, CS Staff can manage our storage more efficiently. For more details, please see the Disk Space page. To request disk space, use the "Project Disk Space" form link on the left. Note that if you specify additional project members in the request form, we will automatically create a unix group consisting of you and the listed users and set the setgid flag on the project directory.
Project Web Space - To set-up a web page or web site for the project, first request project disk space and then use the "Project Web Space" form to the left to request that a subdirectory of the project space be mapped to a web URL. Project web space will give you the ability to host your research group or project-related content at its own subdomain (e.g. http://project.cs.princeton.edu/ ). Even if you are only requesting project disk space for the sole purpose of hosting a project web site, we recommend that you choose a subdirectory (e.g., public_html ) within the project disk space. This will give you the flexibility in the future to also use the project disk space for other purposes.
Project Database - If your project needs a MySQL database (perhaps as a back-end store for a web site), use the "Database" request form at the left and specify a collaborative database.
Mailing Lists - Research projects typically create one or more mailing lists to manage their communication.
Source Repository - If your group will be collaboratively developing code or writing papers, you may want to request an SVN repository from OIT (requires Princeton OIT authentication).
Rack Space for Servers - If you have physical rack-mount servers, they can be housed either in Room 002 of the CS Building or at the University data center at 151 Forrestal . Contact CS Staff for availability and additional details.
Role Accounts / Mail Aliases - please note that we do not create role accounts or provide email aliases. By properly configuring access control, role accounts should not be necessary. Email aliases can be mimicked by requesting a mailing list and selecting the "Mail Alias" type in the form.
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Research projects, you are here.

Undergraduate Research

Below are links to the home pages for a variety of departmental research projects and groups that are typically looking for undergraduate researchers.

A system that implements a constructive theory of types. NuPrl provides both a formal system of mathematics and a programming language. It allows the user to express a wide variety of proof and program-building methods as metalevel programs of the system and use these to construct mathematical theorems and evaluate their computational content.

More to come...this page is under development.

Available Research Projects

Modeling Bird Wings

Faculty Member Michael Neff (in collaboration with Christina Harvey, MAE) Description The goal of this work is to develop a generalized model of the skeleton structure of bird wings that can be adapted to represent a broad range of bird species from albatrosses to hummingbirds. Such a model can be used to generate bird animations and also support research on bird physiology and bio-inspired aircraft design. The challenge is to adjust the skeleton based on real bird data to alter the bone length and rotation range of the joints in order to match the morphology and range of motion of different species. The project should also create a visualization of the skeletons. This work will be completed in consultation with an animator who has worked on numerous blockbuster movies.

This work can be scoped as either a project or dissertation. Requirements - Mathematical maturity - Ability to work independently - Programming skills (C++ is desirable, but other languages are possible) - Experience with computer graphics and/or animation is desirable How to Apply: Please email a brief statement of interest, a description of any relevant experience and your transcript to [email protected] and cc [email protected] .

Real-time robust surgical instrument segmentation using computer vision and deep learning techniques

Faculty Member Laura Marcu Description The Marcu lab in the Department of Biomedical Engineering ( https://marculab.bme.ucdavis.edu/ ) is looking for a highly motivated graduate student (MSc.) with an interest in machine vision to develop real-time surgical instrument segmentation using deep learning techniques. Our lab develops fluorescence lifetime-based imaging instruments and software for intraoperative cancer detection and visualization. Detection and tracking of surgical instruments are prerequisites for proper visualization and potential robotic assisted interventions. Deep learning-based algorithm has been developed but need to be improved for robustness and repackaged to C++ to enable reliable real-time segmentation. Requirements A strong background in computer science and advanced programming skills (Python and C++) are required. The student will work closely with the postdoctoral researchers in the lab to improve existing algorithm and convert it from python to C++ for real-time medical instrument segmentation for intraoperative use.

Related publication can be found at: https://opg.optica.org/boe/fulltext.cfm?uri=boe-11-9-5166&id=437418 . How to Apply: Qualified candidates can apply by sending their CV and a short statement of research interests to Prof. Laura Marcu ( [email protected] ) and cc Dr. Xiangnan Zhou ( [email protected] ).

GPU-accelerated Block Matching Algorithm for motion correction of medical images

Faculty Member Laura Marcu Description The Marcu lab in the Department of Biomedical Engineering at UC Davis ( https://marculab.bme.ucdavis.edu/ ) is looking for a highly motivated graduate student (MSc.) with an interest in image processing to develop GPU-accelerated motion correction algorithm for real time motion correction of medical images. Our lab develops imaging instrumentations and software for intraoperative cancer detection and visualization. Motion correction based on block matching has been developed but need to be refactored to enable GPU-accelerated computation. Requirements A strong background in computer science and advanced programming skills (MATLAB and C++) are required. The student will work closely with the postdoctoral researchers in the lab to refactor existing code to enable GPU-accelerated motion correction.

EthikOS, on supporting Deontological Computing

Faculty Member Felix Wu Description The current AI/ML technology development, such as Transformer/GPT, is intrinsically following the concept of Utilitarianism. Under this project, we are exploring another avenue regarding how to design a new computational paradigm based on Deontology, following three categorical imperatives under the ethics theory by Immanuel Kant. Requirements Must be familiar with at least one programming language and JSON. Knowledge of network protocols will help in the ILOT (Internet-Less of Things) sub-project under EthikOS. Interest in Social and Humanity sciences is very desirable. How to Apply: If interested, please contact Prof. Felix Wu at [email protected] .

LLNL Code Structure Project

Description LLNL is looking for a CS Masters student looking for an MS project. The project entails constructing the code structure and working through challenging implementation of a method for particle trajectories and surface impact in a challenging high flow gradient and high aspect ratio mesh. Required is a C++ code structure that supports CPU parallelization, and optional GPU parallelization. The student will be working in a team environment. The project PI is senior scientist, Dr. Kambiz Salari. Dr. Salari has developed the particle trajectory methodology that is currently utilized in a Mathematica code. The student will have some mentoring from a mid-career computer scientist that has initiated the C++ code structure and CS/math experts that support solvers and algorithm libraries for application in the C++ code. In addition, we have a UCD AME PhD Student employee (advisor is Prof. Seongkyu Lee) who is working with Dr. Salari to perform the code V&V alongside the proposed CS MS student doing code development and parallel scaling performance studies. CS effort includes version control and documentation of implementation, user manual, and installation guide. US citizenship is required for this project. Requirements - Interest in scientific computing - Strong C++ skills - Some experience in parallel computing and performance testing - Some experience in version control - Good verbal communication and writing skills How to Apply: If interested, please send an email to Rose Mccallen [email protected] with your CV.

Scientific software development for DNA nanotechnology

Faculty Member David Doty Description The project will involve:

1. scientific software development, for instance on scadnano ( https://github.com/UC-Davis-molecular-computing/scadnano#readme ) for structural DNA nanotech design, and nuad ( https://github.com/UC-Davis-molecular-computing/nuad#readme ) for DNA sequence design.

2. (optional for MS project) Algorithmic and modeling research in support of DNA sequence design.

3. (optional for MS project) Collaborations with partner institutions on wet-lab experiments in nucleic acid strand displacement and self-assembly to tune the modeling and design software.

Requirements Background in computer science/computer engineering/software engineering (either through a formal degree, or experience with programming) How to apply : Contact David Doty at [email protected] and indicate your background with software development.

Graph Neural Network Modeling of Fintech Networks

Faculty Member Pantelis Loupos Description Graph Neural Networks (GNNs) is a new exciting deep neural network approach to perform analysis and inference on graphs. In this project, you will apply GNNs on fintech networks such as Venmo and other ERC-20 blockchain tokens. Potential research questions include but are not limited to: 1) detect fraudulent activity, 2) what makes a successful fintech network in terms of customer adoption, etc. Requirements - Strong Python skills - Willingness to learn Pytorch and Pytorch Geometric. How to Apply: Please email your resume at [email protected] with title “Application for Graph Neural Network Modeling of Fintech Networks MS Project”

Analysis and Visualization of Unstructured Climate Data

Faculty Member Paul Ullrich Description Professor Paul Ullrich is leading a team to develop tools for analysis and visualization of climate data, particularly global, unstructured climate datasets defined in spherical geometry. These tools are widely employed throughout the climate science community, including the U.S. Department of Energy, the National Oceanic and Atmospheric Administration, and National Center for Atmospheric Research. Interested students can work with Prof. Ullrich and his team to develop new visualization or analysis capabilities in C++ or Python. Our core software repositories for this project can be found at: https://github.com/SEATStandards/ncvis https://github.com/UXARRAY/uxarray Requirements - If interested in visualization of climate data, experience with C++ - If interested in analysis of climate data, experience with Python - Familiarity with Linux operating systems - Basic familiarity with version control (Git) How to apply: If intersted, email Prof. Paul Ullrich ( [email protected] )

Fairness in Machine Learning

Faculty Member Norm Matloff Description Development of ML algorithms for detection and mitigation of bias against protected groups. Requirements A deep, intuitive understanding of ML predictive methods. Merely knowing how to call functions in ML library is not enough. Background in analysis of real data helpful. How to apply: E-mail [email protected] .

Machine Learning Assisted Gamification for Education

Faculty Member Setareh Rafatirad Description This project aims at developing a machine learning assisted gamification framework to promote equity and inclusion in education. Requirements Applicants need to have experience with python programming and a basic machine learning experience. How to apply: Please contact Prof. Setareh Rafatirad [email protected] and send your resume and transcript and the reason for choosing this research topic.

Python programming for physics modeling

Faculty Member Emilie Roncali

Description The Roncali lab in the Department of Biomedical Engineering at UC Davis ( https://roncalilab.engineering.ucdavis.edu/ ) is looking for a highly motivated graduate student (MSc.) with an interest in medical physics and AI programming. Our lab develops physics simulation and AI-based simulations (e.g. GANs), which need to be refactored in Python, specifically for GPU computing.

Requirements A strong background in computer science and advanced programming skills (Python) are required. The candidate should be familiar with Matlab and C++. The student will work closely with the postdoctoral researchers in the lab to translate their code in Python, implementing good programming practice. Qualified candidates can apply by sending their CV and a short statement of research interests to Dr. Emilie Roncali ( [email protected] ).

Using Deep Neural Networks to develop in silico neuronal models

Faculty Member Roy Ben-Shalom Description The goal of this project is to predict the biophysical properties of a neuron based on its electrophysiological response to stimuli. We built a deep learning convolutional network to predict the free parameters of a neuron model given its voltage response to a set of stimuli. Trainees in this project will have the opportunity to interact with various stages of the machine learning process, from data generation, analysis to neural network training.

For this project we are looking for students with an interest in machine learning, optimization, statistics and/or neuroscience with good programming skills to help us improve the algorithm, generate training data, and increase the accuracy of our models. https://www.biorxiv.org/content/10.1101/727974v1

Requirements Background in DL and interest to learn neuroscience. To apply, please email [email protected].

Video-based quantification of dexterous finger movement kinematics using computer vision and deep learning techniques

Faculty Member Wilsaan Joiner and Karen Moxon Description This project will apply computer vision and deep learning techniques to analyze the dexterous finger movements of nonhuman primates ( rhesus macaque monkeys). The subjects are recorded while performing a task which involves retrieving food rewards from variously-oriented shallow wells (i.e., the Brinkman Board task). The MS student is expected to assist in streamlining the analysis of the videos and applying DeepLabCut, a deep learning toolset that allows for the markerless tracking of various locations across multiple video frames. The information obtained from movement tracking will then be used to quantify several features of finger movements (separation, extension and preshaping) in order to provide behavioral measures that are sensitive to injury (e.g., spinal cord contusion) and treatments. Importantly, this will provide critical information to evaluate the effectiveness of novel interventions for clinical conditions that affect the motor system. Requirements

Applicants should have expertise in machine learning, deep learning and computer vision concepts, and ample experience with common programming languages such as C++, Python and Matlab. To apply, please email your CV and interest statement to: [email protected]

Portable Sensor System to Assess the Health Conditions of Individuals working Under Harsh Environments

Faculty Member Cristina Davis Description This project aims to design, prototype, and test an integrated sensor platform that will record physiological data (e.g., heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response) of athletes and individuals who work in harsh environments. The envisioned lightweight device will consist of several commercially available sensors and a microcontroller for physiological data acquisition and integration. A standalone, portable, and small single-board computer (e.g., Raspberry Pi, or alternative) will complement the device for analyzing the extracted data based on prebuilt machine learning models. The system will report data by bluetooth to a WiFi connection hub. Requirements -The applicant from a computer science background should have a solid knowledge in data structures and algorithms -The applicant from a electrical engineering background should know microcontroller coding and circuit designs -Willingness to adapt to several programming languages -Team work may be required To apply, please email your CV and interest statement to: [email protected] .

ResilientDB: Global Scale Resilient Blockchain Fabric

Faculty Member Mohammad Sadoghi Description Sadoghi’s research group focuses on all facets of building secure and massive-scale data management. We aim to pioneer a next-generation resilient data platform at scale, a distributed ledger centered around a democratic and decentralized computational model, named ResilientDB Blockchain Fabric . At the heart of blockchain lies the problem of consensus, which is at the forefront of our research and development in ResilientDB. Currently, we are investigating many exciting directions such as speculative consensus, concurrent & parallel consensus, hardware-accelerated consensus (e.g., SGX or RDMA), view-change-less consensus, reconfigurable consensus, hybrid consensus (e.g., BFT + PoS + PoW), and a wide array sharding and cross-chain protocols. To learn more, we invite you to review ResilientDB Blog , Wiki , Codebase , Hands-on Tutorial , Publications , and Roadmap . We are seeking creative students who aim to be independent thinkers with controversial ideas. Funding may be available for exceptional students upon demonstration of solid progress. Requirements -Strong C/C++ skills are a must -Experience with operating systems, distributed systems, database transactions, concurrency controls, multi-threaded programming, and synchronization would be terrific To join us at ExpoLab, please email your resume to Prof. Sadoghi, [email protected] .

SSL-Pathology: Semi-supervised Learning in Pathology Detection of Alzheimer's Disease

Faculty Member Chen-Nee Chuah Description While supervised learning (SL) techniques such as convolutional neural networks achieve promising results in medical images, procuring a sufficiently large dataset with annotations is labor-intensive, especially in gigapixel pathology images. To circumvent the need for large labeled datasets, semi-supervised learning (SSL) can be a potential approach. Amyloid-beta plaques are hallmarks of Alzheimer's disease. A supervised detection model has been established to classify three types of plaques. However, it relies on more than 50,000 annotated images for training the model. In this project, we will adopt SSL to this problem and explore the upper bound of SSL to relieve the reliance on a large labeled dataset. Requirements Expertise in machine learning concepts, Docker, and Python programming inclusive of scikit-learn, Pandas, PyTorch/Tensorflow.

If interested, please email your resume/CV to [email protected] with [SSL] in the subject title.

CeDP: Computational Efficiency of Deep Learning in Digital Pathology

Faculty Member Chen-Nee Chuah Description While supervised learning (SL) techniques such as convolutional neural networks achieve promising results in pathology images, the computational complexity is still significantly heavy due to the gigapixel resolution of pathology images. To make deep learning more practical in digital pathology, it is necessary to comprehensively study the tradeoff between performance and complexity. In this project, we will study how to deploy efficient deep learning models on edge devices for pathology image analysis and how to remove unnecessary computation in the recent state-of-the-art deep learning networks. We will also benchmark the complexity of different models on our pathology datasets. Requirements Expertise in machine learning concepts, Docker, and Python programming inclusive of scikit-learn, Pandas, PyTorch/Tensorflow.

If interested, please email your resume/CV to [email protected] with [CeDP] in the subject title.

Augmented Reality Quadcopter Game Control

Faculty Member Nelson Max

Description Professor Nelson Max is leading a team to develop a quadcopter-based augmented reality video game, in which the players pilot quadcopters “first-person”, viewing an AR game environment through a head-mounted display. The team is seeking a student to continue development of the quadcopter control system using the Robot Operating System (ROS). The student will be responsible for improving the existing control algorithm and interfacing the control algorithm to the Unity game engine to coordinate the real and virtual game experiences. The student will collaborate with other team members responsible for game design and quadcopter localization.

Requirements Required ♦ Python programming experience ♦ C++ programming experience ♦ Familiarity with Linux operating systems (Ubuntu) ♦ Basic familiarity with version control (Git) ♦ Strong skills in troubleshooting Linux software Preferred ♦ Familiarity with Robot Operating System (ROS) ♦ Familiarity with ArduPilot and/or PX4 autopilot firmware, MAVLink communication protocol ♦ Experience piloting consumer drones ♦ Basic familiarity with computer networking

Gunrock, Parallel Graph Analytics on GPUs

Faculty Member John Owens

Description John Owens’ research group focuses on GPU computing and has a large project on parallel graph analytics called Gunrock. We have a large need for application development on Gunrock, writing interesting graph applications that use our framework (we have a long list of these from our funding agency). We would hope to train you in GPU computing and in using our framework. This could potentially lead to MS thesis opportunities but also could be a shorter project with an option of switching to another group if interested. We need talented students who can learn quickly and work independently. Funding may be available.

Requirements ♦ Strong C/C++ skills are a must ♦ Experience with parallel computing would be terrific, but not required

Multiplayer Augmented Reality Quadcopter Game System

Description Dr. Max is looking for more master’s students to help with our multiplayer augmented reality quadcopter game system. The system includes for each game player a Solo 3DR quadcopter with a mounted GoPro 4 Black video camera, a computer with an NVIDIA GTX 1070 GPU, Oculus Rift VR goggles, Oculus Touch hand held controllers for flying the drone, and wireless communication links. Using markers in the environment, as seen by the video cameras, the computers determine the position of each quadcopter, and use the inertial sensors and quadcopter physics simulation to extrapolate to future frames to decrease VR latency. The games are written in Unity. The quadcopter positions are communicated to the master computer, and are used in the game physics calculations. The master computer receives the user control signals, and either sends them directly to the quadcopter, or modifies them according to the game physics and to avoid collisions. This centralized master server also contain the game logic, such as scoring.

The video camera is fixed on the drone, with a wide angle lens so that the part of the image can be selected appropriate to the user head position and orientation. The computer graphics (CG) augmented elements are added in stereo onto the real video background, also accounting for the user head motion. Thus the game players feel as if they were looking through the windows of a real aircraft at the actual environment in which they are flying. We are using the Oculus Rift software development environment, which allows the video input and computer graphics elements to be supplied on separate layers, with different updates and motion extrapolation parameters. Using the known quadcopter positions, the images of the other quadcopters in the video background can be covered up with stereo CG models, so that they also appear in 3D.

Our initial game was a pong-like paddleball game, with a paddle at each quadcopter, and a virtual ball, which we hope to replace with a third quadcopter. There are game displays showing top down and side views, either on the cockpit dashboard or in a heads-up display on its window, and sound effects when the ball is hit by the paddle, or hits the walls, floor, or ceiling of the game space. Our second game was a maze racing game, where two players start at opposite corners of a two-level maze like track, and attempt to overtake each other.

We are now developing a shooting game, where each player has a gun to shoot opponents, and the controlling computer decides when an opponent has been hit, adding appropriate graphics like fire. The projectiles are shown in stereo CG. When a player’s quadcopter has been disabled, the computer will take control of its flight and bring it to a safe landing. We are also evolving the paddle-ball game into a 3D soccer game, with goal areas on two opposite walls, which will light up when there is a goal.

Aspects of the system development which could lead to Master’s projects are:

The computer vision system for Simultaneous Location and Mapping (SLAM) The control of the quadcopter, including anticipating and preventing collisions Creating new games, for example, 3D billiards Requirements ♦ Continuing or admitted Master’s student in the graduate program in Computer Science

Research Opportunities

Undergraduate research in computer science.

For specific information on undergraduate research opportunities in Computer Science visit https://csadvising.seas.harvard.edu/research/ .

General Information about Undergraduate Research

Opportunities for undergraduates to conduct research in engineering, the applied sciences, and in related fields abound at Harvard. As part of your coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to take part in or participate in some extraordinary projects covering topics ranging from bioengineering to cryptography to environmental engineering.

Our dedicated undergraduate research facilities and Active Learning Labs also provide opportunities for students to engage in hands-on learning. We encourage undergraduates from all relevant concentrations to tackle projects during the academic year and/or over the summer.

Keep in mind, many students also pursue summer research at private companies and labs as well as at government institutions like the National Institutes of Health.

If you have any questions, please contact or stop by the Office of Academic Programs, located in the Science and Engineering Complex, Room 1.101, in Allston.

Research FAQs

The SEAS website has a wealth of information on the variety of cross-disciplinary research taking place at SEAS. You can view the concentrations available at SEAS here , as well as the research areas that faculty in these concentrations participate in. Note that many research areas span multiple disciplines; participating in undergraduate research is an excellent way to expand what you learn beyond the content of the courses in your concentration!

To view which specific faculty conduct research in each area, check out the All Research Areas section of the website. You can also find a helpful visualization tool to show you the research interests of all the faculty at SEAS, or you can filter the faculty directory by specific research interests. Many faculty’s directory entry will have a link to their lab’s website, where you can explore the various research projects going on in their lab.

The Centers & Initiatives page shows the many Harvard research centers that SEAS faculty are members of (some based at SEAS, some based in other departments at Harvard).

Beyond the website, there are plenty of research seminars and colloquia happening all year long that you can attend to help you figure out what exactly you are interested in. Keep an eye on the calendar at https://events.seas.harvard.edu !

There are several events that are designed specifically for helping undergraduate students get involved with research at SEAS, such as the Undergraduate Research Open House and Research Lightning Talks . This event runs every fall in early November and is a great opportunity to talk to representatives from research labs all over SEAS. You can find recordings from last year’s Open House on the SEAS Undergraduate Research Canvas site .

Most of our faculty have indicated that curiosity, professionalism, commitment and an open mind are paramount. Good communication skills, in particular those that align with being professional are critical. These skills include communicating early with your mentor if you are going to be late to or miss a meeting, or reaching out for help if you are struggling to figure something out. Good writing skills and math (calculus in particular) are usually helpful, and if you have programming experience that may be a plus for many groups. So try to take your math and programming courses early (first year) including at least one introductory concentration class, as those would also add to your repertoire of useful skills.

Adapted from the Life Sciences Research FAQs

Start by introducing yourself and the purpose of your inquiry (e.g. you’d like to speak about summer research opportunities in their lab). Next, mention specific aspects of their research and state why they interest you (this requires some background research on your part). Your introduction will be stronger if you convey not only some knowledge of the lab’s scientific goals, but also a genuine interest in their research area and technical approaches.

In the next paragraph tell them about yourself, what your goals are and why you want to do research with their group. Describe previous research experience (if you have any). Previous experience is, of course, not required for joining many research groups, but it can be helpful. Many undergraduates have not had much if any previous experience; professors are looking for students who are highly motivated to learn, curious and dependable.

Finally, give a timeline of your expected start date, how many hours per week you can devote during the academic term, as well as your summer plans.

Most faculty will respond to your email if it is clear that you are genuinely interested in their research and have not simply sent out a generic email. If you don’t receive a response within 7-10 days, don’t be afraid to follow up with another email. Faculty are often busy and receive a lot of emails, so be patient.

There are several ways that undergraduate research can be funded at SEAS. The Program for Research in Science and Engineering ( PRISE ) is a 10-week summer program that provides housing in addition to a stipend for summer research. The Harvard College Research Program ( HCRP ) is available during the academic year as well as the summer. The Harvard University Center for the Environment ( HUCE ) has a summer undergraduate research program. The Harvard College Office of Undergraduate Research and Fellowships ( URAF ) has more information on these, as well as many other programs.

Students that were granted Federal Work Study as part of their financial aid package can use their Work Study award to conduct undergraduate research as well (research positions should note that they are work-study eligible to utilize this funding source).

Research labs may have funding available to pay students directly, though we encourage you to seek out one of the many funding options available above first.

Yes! Some students choose to do research for course credit instead of for a stipend. To do so for a SEAS concentrations, students must enroll in one of the courses below and submit the relevant Project Application Form on the Course’s Canvas Page:

Applied Mathematics 91r (Supervised Reading and Research)
Computer Science 91r (Supervised Reading and Research)
Engineering Sciences 91r (Supervised Reading and Research)

In general, you should expect to spend a minimum of one semester or one summer working on a project. There are many benefits to spending a longer period of time dedicated to a project. It’s important to have a conversation early with your research PI (“Principal Investigator”, the faculty who runs your research lab or program) to discuss the intended timeline of the first phase of your project, and there will be many additional opportunities to discuss how it could be extended beyond that.

For students who are satisfied with their research experience, remaining in one lab for the duration of their undergraduate careers can have significant benefits. Students who spend two or three years in the same lab often find that they have become fully integrated members of the research group. In addition, the continuity of spending several years in one lab group often allows students to develop a high level of technical expertise that permits them to work on more sophisticated projects and perhaps produce more significant results, which can also lead to a very successful senior thesis or capstone design project.

However, there is not an obligation to commit to a single lab over your time at Harvard, and there are many reasons you may consider a change:

your academic interests or concentration may have changed and thus the lab project is no longer appropriate
you would like to study abroad (note that there is no additional cost in tuition for the term-time study abroad and Harvard has many fellowships for summer study abroad programs)
your mentor may have moved on and there is no one in the lab to direct your project (it is not unusual for a postdoctoral fellow who is co-mentoring student to move as they secure a faculty position elsewhere)
the project may not be working and the lab hasn’t offered an alternative
or there may be personal reasons for leaving. It is acceptable to move on

If you do encounter difficulties, but you strongly prefer to remain in the lab, get help. Talk to your PI or research mentor, your faculty advisor or concentration advisor, or reach out to [email protected] for advice. The PI may not be aware of the problem and bringing it to their attention may be all that is necessary to resolve it.

Accepting an undergraduate into a research group and providing training for them is a very resource-intensive proposition for a lab, both in terms of the time commitment required from the lab mentors as well as the cost of laboratory supplies, reagents, computational time, etc. It is incumbent upon students to recognize and respect this investment.

One way for you to acknowledge the lab’s investment is to show that you appreciate the time that your mentors set aside from their own experiments to teach you. For example, try to be meticulous about letting your mentor know well in advance when you are unable to come to the lab as scheduled, or if you are having a hard time making progress.
On the other hand, showing up in the lab at a time that is not on your regular schedule and expecting that your mentor will be available to work with you is unrealistic because they may be in the middle of an experiment that cannot be interrupted for several hours.
In addition to adhering to your lab schedule, show you respect the time that your mentor is devoting to you by putting forth a sincere effort when you are in the lab. This includes turning off your phone, ignoring text messages, avoiding surfing the web and chatting with your friends in the lab etc. You will derive more benefit from a good relationship with your lab both in terms of your achievements in research and future interactions with the PI if you demonstrate a sincere commitment to them.
There will be “crunch” times, maybe even whole weeks, when you will be unable to work in the lab as many hours as you normally would because of midterms, finals, paper deadlines, illness or school vacations. This is fine and not unusual for students, but remember to let your mentor know in advance when you anticipate absences. Disappearing from the lab for days without communicating with your mentor is not acceptable. Your lab mentor and PI are much more likely to be understanding about schedule changes if you keep the lines of communication open but they may be less charitable if you simply disappear for days or weeks at a time. From our conversations with students, we have learned that maintaining good communication and a strong relationship with the lab mentor and/or PI correlates well with an undergraduate’s satisfaction and success in the laboratory.
Perhaps the best way for you to demonstrate your appreciation of the lab’s commitment is to approach your project with genuine interest and intellectual curiosity. Regardless of how limited your time in the lab may be, especially for first-years and sophomores, it is crucial to convey a sincere sense of engagement with your project and the lab’s research goals. You want to avoid giving the impression that you are there merely to fulfill a degree requirement or as a prerequisite for a post-graduate program.

There are lots of ways to open a conversation around how to get involved with research.

For pre-concentrators: Talk to a student who has done research. The Peer Concentration Advisor (PCA) teams for Applied Math , Computer Science and Engineering mention research in their bios and would love to talk about their experience. Each PCA team has a link to Find My PCA which allows you to be matched with a PCA based on an interest area such as research.
For SEAS concentrators: Start a conversation with your ADUS, DUS, or faculty advisor about faculty that you are interested in working with. If you don’t have a list already, start with faculty whose courses you have taken or faculty in your concentration area. You may also find it helpful to talk with graduate student TFs in your courses about the work they are doing, as well as folks in the Active Learning Labs, as they have supported many students working on research and final thesis projects.
For all students: Attend a SEAS Research Open House event to be connected with lab representatives that are either graduate students, postdocs, researchers or the PI for the labs. If you can’t attend the event, contact information is also listed on the Undergraduate Research Canvas page for follow-up in the month after the event is hosted.

For any student who feels like they need more support to start the process, please reach out to [email protected] so someone from the SEAS Taskforce for Undergraduate Research can help you explore existing resources on the Undergraduate Research Canvas page . We especially encourage first-generation and students from underrepresented backgrounds to reach out if you have any questions.

In Computer Science

First-Year Exploration
Concentration Information
Secondary Field
Senior Thesis
AB/SM Information
Student Organizations
How to Apply
PhD Timeline
PhD Course Requirements
Qualifying Exam
Committee Meetings (Review Days)
Committee on Higher Degrees
Research Interest Comparison
Collaborations
Cross-Harvard Engagement
Lecture Series
Clubs & Organizations
Centers & Initiatives
Alumni Stories

Did you know that you can pursue a research project you like, working directly with CS faculty and PhD candidates, and get 3 points of credit to count towards your CS degree?

Interested? Then...

Choose a project you find fascinating

Computer Science Projects Listed Under the Student Research Involvement Portal

AVAILABLE RESEARCH PROJECTS in MICE

Contact the professor, introduce yourself and ask if you can work for the project

You can also browse available projects , and network with faculty to find the project that's right for you, at the Undergraduate and MS Project Fair ! Come to the CS Lounge (450 Mudd) Wednesday and Thursday, January 18 and 19, 2017, at 12:00PM-2:00PM and be sure to bring your resume and a copy of your official transcript. For a list of projects and their days, click here.

Masters students: Register for E6901. You can count up to 12 points of project courses toward your degree.
Undergraduates: Register for COMS W3998 for your first project. For your second project, register for W4901. You can count up to 6 points of project courses toward your CS degree.

Contact the Research Project Liaisons - project [at] lists [dot] cs [dot] columbia [dot] edu

Yuan J Kang - [first name] [middle initial] [last initial] [at] cs [dot] columbia [dot] edu

Asynchronous Circuits and Systems Group
Autonomous Agents Lab
Columbia Automated Vision Environment
Columbia Vision and Graphics Center
Computational Biology
Computer Graphics Group
Center for Computational Learning Systems
Computer Graphics and User Interfaces Laboratory
Database Research Group
Distributed Computing and Communications Laboratory
Distributed Network Analysis Research Group
Information-Based Complexity
Internet Real-Time Laboratory
Intrusion Detection Systems
Languages and Compilers Group
Machine Learning
Natural Language Processing Group
Network Computing Laboratory
Network Security Laboratory
Programming Systems Laboratory
Reliable Computer Systems
Robotics Laboratory
Spoken Language Processing Group
Theory of Computing Group

Quantum Computing Education for Computer Science Students: Bridging the Gap with Layered Learning and Intuitive Analogies † † thanks: This research was conducted as part of the QCloud QuantumEd project led by Munster Technological University and funded by the EOSC Future project I ⁢ N ⁢ F ⁢ R ⁢ A ⁢ E ⁢ O ⁢ S ⁢ C − 03 − 2020 𝐼 𝑁 𝐹 𝑅 𝐴 𝐸 𝑂 𝑆 𝐶 03 2020 INFRAEOSC-03-2020 italic_I italic_N italic_F italic_R italic_A italic_E italic_O italic_S italic_C - 03 - 2020 - Grant Agreement Number 101017536 101017536 101017536 101017536 . This publication was supported in part by the CyberSkills HCI Pillar 3 Project 18364682. Dr Murray and Dr Mjeda acknowledge support from Science Foundation Ireland co-funded from the European Regional Development Fund under Grant 13 / R ⁢ C / 2077 ⁢ _ ⁢ P ⁢ 2 13 𝑅 𝐶 2077 _ 𝑃 2 13/RC/2077\_P2 13 / italic_R italic_C / 2077 _ italic_P 2 and 13 / R ⁢ C / 2094 P ⁢ 2 13 𝑅 𝐶 subscript 2094 𝑃 2 13/RC/2094_{P}2 13 / italic_R italic_C / 2094 start_POSTSUBSCRIPT italic_P end_POSTSUBSCRIPT 2 respectively.

Quantum computing presents a transformative potential for the world of computing. However, integrating this technology into the curriculum for computer science students who lack prior exposure to quantum mechanics and advanced mathematics remains a challenging task. This paper proposes a scaffolded learning approach aimed at equipping computer science students with essential quantum principles. By introducing foundational quantum concepts through relatable analogies and a layered learning approach based on classical computation, this approach seeks to bridge the gap between classical and quantum computing. This differs from previous approaches which build quantum computing fundamentals from the prerequisite of linear algebra and mathematics. The paper offers a considered set of intuitive analogies for foundation quantum concepts including entanglement, superposition, quantum data structures and quantum algorithms. These analogies coupled with a computing-based layered learning approach, lay the groundwork for a comprehensive teaching methodology tailored for undergraduate third level computer science students.

Index Terms:

I introduction.

Quantum computing is an emerging field with the potential to revolutionize the world of computing. Its rapid advancement into a mainstream and commercial technology [ 1 , 2 ] makes it essential to equip computer science students with the skills and knowledge to harness its potential. However, the typical computer science student has no prior knowledge of quantum mechanics and students often struggle to grasp the quantum computing concepts which are fundamentally different from classical computing. Hence, it is crucial to develop teaching and learning approaches tailored to teach quantum computing concepts to computer science students.

This paper proposes a layered learning approach, emphasizing the grounding of quantum concepts in classical computation and intuitive analogies. We posit that to teach quantum computing effectively to computer science students without a background in quantum mechanics, we need a layered (scaffolded) approach that builds on the existing knowledge of classical computing and underpins the quantum computing upskilling with foundational knowledge . In this paper, a curriculum introducing fundamental quantum computing topics is outlined which is scaffolded from classical computing concepts. This differs from existing approaches which either require advanced pre-requisites in physics or ground their foundations in linear algebra [ 3 ] .

Analogies are a powerful teaching methodology for conveying details of complex concepts. They are particularly valuable to quantum mechanics where concepts are often at odds with our classical interpretations. However, Didics [ 4 ] found that educators often rely on spur-of-the-moment creation of analogies which, at times, may not accurately represent the chosen concept. Therefore, in the second part of this paper we outline an initial collection of analogies which can be used to explain core quantum topics including entanglement, superposition, quantum data structures and quantum cryptography algorithms. This approach ensures a cohesive and accessible understanding of quantum concepts, rather than relying on the creation of ad-hoc analogies during classes.

Overall, the goal of this paper is to assist educators in their creation and delivery of accessible and informative quantum education to computer science students. Bridging the gap between quantum computing and tradition computer science allows computing students to contribute to the next generation of the computation which will revolutionise how we interpret and analyse data, consider cybersecurity and understand the world around us.

The paper is laid out as follows: Section II provides a background on existing quantum education literature. Section III describes current education approaches including a review of existing international quantum education curriculum content. Section IV outlines our suggested layered learning curriculum for introducing quantum foundations to computer science students. In each of the layers, we build on classical foundations and describe useful analogies to convey the increasingly complex topics. Table I provides intuitive descriptions of two pivotal quantum algorithms. Tables II – V include a collection of tailored analogies for the explanation of quantum data structures, superposition, quantum gates and entanglement respectively. The paper finishes with a description of future work and conclusions in Section V .

II Literature review

Higher education for careers in quantum industry has predominantly been the domain of PhD programs in physics departments, typically with a focus on proof-of-principle quantum experiments [ 5 ] . Engaging a diverse range of degree subjects and levels can significantly expand the talent pool and enhance participation and retention in the quantum workforce. This being especially poignant as we seek to transition into marketable quantum products that address real-world challenges [ 6 , 5 ] . Computer science education spaces appear to be the natural milieu where to invest in the development of the current and future workforce [ 7 , 3 ] but the current reality presents significant challenges. The conceptual and mathematical foundations established in physics courses tend to serve as the basis of quantum computing [ 5 , 8 ] while in many cases software students can enter and finish a computer science degree with no previous physics education and a limited mathematics background. This gap in foundational knowledge can create difficulties in grasping the complex terminologies and approaches commonly used in existing teaching resources and scientific papers, acting as a substantial barrier [ 9 , 8 , 10 ] .

In response to these challenges, introductory lectures on quantum computing have been developed by several researchers [ 11 , 12 , 7 , 9 , 13 , 14 , 8 ] , including the lectures from CERN [ 9 ] which were underpinned by the principles of minimizing the prerequisites and emphasizing the practical implementation of any quantum protocols and algorithms discussed in the course. When teaching a quantum computing course without prerequisites in physics or mathematics, Temporão et al. [ 3 ] observed that a significant portion of the curriculum focused on fundamental Linear Algebra and essential concepts of quantum physics. In an effort to make quantum computing accessible to a wider audience by eliminating prerequisites to join the course, [ 8 ] employ a visual representation alongside a spiral curriculum. In their visual representation [ 8 ] they replace bra-ket notation with the analogy of a white ball representing | 0 ⟩ ket 0 \ket{0} | start_ARG 0 end_ARG ⟩ , and a black ball representing | 1 ⟩ ket 1 \ket{1} | start_ARG 1 end_ARG ⟩ and only after the core concepts are well understood do they introduce students to the mathematical bra-ket notation. Carrascal et al. [ 7 ] propose a teaching roadmap for quantum computing that begins with an understanding of how information is represented in classical computers, emphasizing concepts such as probability, wavefunctions, and measurement. The curriculum then transitions to testing quantum gates, proceeds to quantum programming, and concludes with an exploration of established quantum algorithms [ 7 ] . In a somewhat outlier approach to teaching quantum science, [ 15 ] advocate an artistic methodology, incorporating gamification and theatre projects as engaging strategies to render quantum science more accessible to the general public.

When exploring effective teaching strategies for quantum computing to software students, the intuition is to first examine the methods employed in teaching complex quantum concepts to physics students [ 16 ] . To clarify the often counter-intuitive phenomena of quantum physics, educators frequently rely on simplifying and idealizing complex processes, incorporating thought experiments, analogies, and various representations. Particularly in the context of quantum physics, where the quantum phenomena do not align with our macroscopic experiences and understanding, the use of analogies becomes crucial [ 16 ] .

Didics [ 4 ] identifies five primary aims for employing analogies in teaching quantum theory in physics: introducing a new topic, clarifying taught concepts, capturing students’ attention, increasing class participation, and comparing classical and quantum physics. Using analogies to clarify concepts accounts for half of all analogies used. Interestingly, there was no systematic use of analogies, with 90% of them being developed spontaneously [ 4 ] . Furthermore, the study reports that the analogies used often rely heavily on specific shared cultural backgrounds, such as national sayings and proverbs [ 4 ] .

Drawing parallels with the teaching of complex quantum concepts in physics, analogies are also employed as a valuable tool in quantum education [ 17 ] . For instance, the concept of superposition, is often taught using the coin toss analogy, where a coin in mid-air represents a superposition of heads and tails. Depending on the students’ backgrounds, be it in physics, mathematics, or engineering, the analogy is then complemented by connecting it to concepts like photon or electron spins, by demonstrations such as the Stern-Gerlach experiment, use of mathematical-symbolic representations, such as vectors, and graphical representations such as the Bloch sphere and unit circles [ 17 ] .

Informed by the existing literature, we posit in this paper the importance of developing effective pedagogical strategies that cater to students with diverse backgrounds, particularly those lacking prerequisites in physics and mathematics. This perspective has guided our work in adopting strategies that minimize prerequisites and emphasize practical implementations to facilitate understanding and engagement. Additionally, this has informed our focus on the development of analogies to teach complex quantum concepts rather than relying on analogies developed on the spur of the moment during classes which tend to not offer a consistent and accessible approach to the understanding of quantum phenomena.

III Teaching Approaches

Iii-a current approaches.

In existing quantum computing education the status quo is to teach students with a physics background some computing topics. However, as quantum computing evolves into a main stream technology with practical and commercial applications, the importance grows for traditional computer science students to gain exposure to this technology.

The authors reviewed existing modules and programmes which aim to provide an introduction to quantum computing for computer science students from eight different universities [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] .

The typical layout for these modules begins with a motivation for the study of quantum computing followed by a review of linear algebra and complex vector spaces in the context of quantum information. This introduction is then followed by the core quantum concepts including quantum bits, quantum gates and quantum properties (entanglement, superposition, measurement). It then typically continues with an analysis of quantum algorithms (such as Deutsch-Jozsa algorithm, Grover’s algorithm, Shor’s algorithm). Finally most modules finish with quantum communication and quantum cryptography applications.

In each of these modules, the goal was to teach quantum computing without a requirement for a background in quantum mechanics. However, instead of a basis in physics, all of these module except [ 25 ] based the fundamentals on linear algebra. In fact in 2022 Temporāo et al. [ 3 ] proposed and tested an introductory quantum computing course where every concept is based on applied linear algebra. This layered learning approach demonstrated that quantum computing can be taught without a prerequisite of physics or advanced quantum mechanism.

While this is an effective layered approach, many computer science students struggle with mathematics and mathematics anxiety [ 26 ] . Women in particular are affected by the impact maths anxiety has on their vocational interests and an effort should be made to avoid further excluding women from this emerging domain [ 27 ] . We postulate that quantum computing can also be taught using computer-science domain specific fundamentals and real world analogies. While it is beneficial, similar to classical computing, students can have strong computing skills irrespective of mathematics skills. The development of quantum computation will require expertise from multiple fields including computer science, engineering, mathematics and physics. Computer scientists can bring expertise to the quantum computing field that augment and complement the skill sets mathematicians and physicists bring.

From the courses we observed, many courses include strict prerequisites: “A strong undergraduate background in linear algebra, discrete probability, and theory of computation. No background in physics is required.” [ 24 ] . The current positioning of quantum computing education for computer scientists as fundamentally of mathematical basis has the potential to exclude a large cohort of computer scientists without this mathematical inclination [ 28 ] .

In this paper we discuss the Layered Learning aspects of our proposed approach with a particular focus on classical computing and analogies as the basis for explaining fundamental concepts rather than mathematics or physics.

III-B “Layered Learning” and Use of Analogies in Education

Traditionally, scaffolding in learning is recognized as a supportive method aimed at fostering student learning. It is a layered learning approach that involves providing structured support to learners as they progress toward mastering a new concept or skill. Similar to the support structure used in construction, scaffolding helps students by breaking down complex tasks into smaller, more manageable steps.

Initially introduced by Wood et al. [ 29 ] in their exploration of tutoring’s influence on children’s problem-solving skills, the concept of scaffolding has been extensively examined and extended by numerous researchers [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ] . Even though the majority of empirical studies investigating scaffolding tend to be limited in scale and are often characterized by descriptive approaches, research in this area has contributed valuable insights and findings. Foremost, the findings indicate that scaffolding is an effective approach for fostering the students’ metacognitive and cognitive activities [ 36 ] . For a theoretical treatment of the scaffolding as a technique, the reader is directed to [ 30 , 35 ] .

In this paper, we present a scaffolded approach to teaching quantum computing concepts. Henceforth, we will refer to scaffolding as the ’Layered Learning Approach.’ This adjustment aims to enhance cross-disciplinary understanding and simplify explanations.

The use of analogies in STEM education has a long history and a strong theoretical and empirical basis. Analogies have been recognized as an essential feature of scientific reasoning and discovery, as scientists often use analogies to generate hypotheses, test predictions, and communicate findings [ 37 , 38 , 39 , 40 , 41 ] . To give one example, Rutherford’s analogy of imagining the atom as a miniature solar system [ 42 ] was so effective that it remains the dominant imagery that comes to mind when thinking of or illustrating an atom. The power of analogies stands in relating complex concepts to familiar situations or phenomena and their use can foster the development of higher order thinking skills, such as analysis, synthesis, evaluation, and creativity [ 41 ] . For a systematic mapping study of use of analogies in science education, the reader is directed to [ 43 ] .

The advantages are contingent upon effective analogies, while, spur-of-the-moment, unplanned analogies, even if well-intentioned, can be misconstrued and prove misleading [ 41 ] . Therefore, we propose using a series of domain-specific analogies to facilitate the learning of key concepts in quantum computing among computer science students.

We propose using these analogies within a Layered Learning Approach that begins with revisiting classical computing foundations, emphasizing strong knowledge of algorithms, data structures, and complexity theory, followed by introducing fundamental quantum principles like qubits, quantum gates, superposition, and entanglement through simplified explanations and analogies from everyday life.

At a glance:

Layer 1: Classical Foundations . Here we start by reinforcing classical computing concepts to ensure students have a strong understanding of algorithms, data structures, and complexity theory. This also where we emphasize the classical-quantum hybrid nature of quantum computing.

Layer 2: Quantum Foundations . The students are introduced to fundamental quantum principles such as qubits, quantum gates, superposition, and entanglement. Simple analogies and where possible visual aids are used to make these abstract concepts more accessible.

IV Layered Learning

Iv-a layer 1: classical foundations.

In this foundational layer, we lay the groundwork for understanding quantum computing by reinforcing classical computing concepts. This approach serves as a bridge between the students’ existing knowledge and the world of quantum computation. It focuses on ensuring that students have a robust understanding especially of those aspects of classical computing that are vital for comprehending the nuances and potential of quantum computing.

IV-A 1 Algorithms

We begin by revisiting and reinforcing core classical algorithmic concepts which are the backbone of computing. Students delve into algorithms for searching, sorting, and problem-solving. For instance, we explore classical sorting algorithms like quicksort and mergesort . Students also engage in interactive discussions on fundamental algorithms that underpin different applications. Case studies relying on challenges such as route planning and network optimization (e.g. the classic problem of finding the shortest path in a graph), are used. Classical algorithms such as Dijkstra’s or Bellman-Ford are explored, laying the groundwork for understanding how quantum algorithms can offer enhancements in areas like optimization.

IV-A 2 Complexity Theory

Complexity theory examines the efficiency and computational limits of algorithms. Students delve into concepts like time and space complexity. This knowledge is essential for evaluating the performance of classical algorithms and understanding the potential improvements quantum algorithms can offer. For example, the students are guided through problems that are hard to solve efficiently by exploring the concept of NP-completeness in classical complexity theory. This is used to contextualise the benefits of quantum algorithms, such as say Shor’s algorithm [ 44 ] for factorization.

Overall, the introduction of quantum algorithms and quantum programming in this layer, serves to contextualise how quantum computing can significantly enhance functionality in various application domains, from searching databases more efficiently to breaking classical cryptographic systems. In this layer, we transition from the fundamental principles of quantum computing to the practical application of quantum algorithms. We introduce students to quantum algorithms such as Grover’s [ 45 ] and Shor’s [ 44 ] algorithms (see Table I ), and we emphasize the importance of hands-on coding. We recommend students are initially introduced to quantum programming via python for example using Qiskit or Ket , a language they would already be familiar with, before other languages such as Silq and Q# are introduced.

IV-A 3 Data Structures

We revisit classical data structures such as arrays, linked lists, and trees and analyse their respective trade-offs when used in classical computing. Subsequently the students are introduced to the ‘equivalent’ quantum data structures and their computational advantages are discussed (see Table II ).

IV-A 4 Classical-Quantum Hybrid Nature

We emphasize how quantum computing complements classical computing rather than replacing it entirely. Exploring the hybrid nature of quantum computing, students learn how classical and quantum components can collaborate to solve complex problems more effectively. They’re encouraged to draw parallels from daily life, like hybrid vehicles using both gasoline and electricity for efficiency in areas lacking complete electric infrastructure. Similarly, in cooking, chefs optimize their process by combining conventional stove-tops with modern tools like sous vide cookers, improving both experience and results. This fusion mirrors how quantum computing integrates classical and quantum elements to enhance computational capabilities, particularly for solving intricate problems more efficiently.

IV-B Layer 2: Quantum Foundations

In our approach to teaching quantum computing to computer science students new to quantum mechanics, the second layer focuses on establishing quantum foundations. This layer is vital, as it highlights core principles without assuming prior quantum knowledge. Using simple analogies bridges the gap between quantum mechanics and the practical world of quantum computing, enhancing accessibility and engagement for computer science students.

IV-B 1 Qubits

A fundamental quantum concept to begin with is the Qubit [ 46 ] , which serves as the quantum analog of classical bits. Qubits have the unique property of existing in a state of superposition, allowing them to represent both 0 and 1 simultaneously. This concept often proves challenging for students, so clear and relatable explanations are essential. To make qubits more understandable, we draw analogies from everyday experiences. For instance, we compare a qubit in superposition to a spinning coin showing both heads and tails at once. In Table III , we provide a collection of analogies for teaching purposes. Visual aids, such as diagrams representing qubit states as vectors, can also aid comprehension. Figure 2 illustrates the Bloch Sphere, where a qubit’s state is represented. For example, a qubit which has a half a chance of measuring as a 0 and half a chance of measuring as a 1 can be visualised as sitting on the equator of the globe, half way between 0 and 1.

All the analogies illustrate the concept of superposition by emphasizing the idea that qubits can represent multiple states at once and that measurement results in the selection of one of those states.

Similarly, we propose analogies to explain quantum logic gates ( Table IV ). We also utilise the visual aid of the Bloch sphere (Figure 2 ) and the analogy of an ice skater on the surface of the sphere to represent the effect each of the gates has on the spin (phase) and position (state) of qubits (Figure 3 ).

IV-B 2 Superposition and Entanglement

Superposition is a defining characteristic of quantum systems and we explain it through the analogies we provided when explaining the qubit ( Table III ).

Entanglement 1 1 1 In the Everettian (quantum physicist Hugh Everett III (1930–1982)) view of quantum physics the concept of entanglement is described via the universal wave function, in other words, positing that the quantum state of the whole universe is ’interlinked’ and can be ’captured’ in one wave function. , another challenging concept, can be illustrated by discussing the behaviour of entangled particles. Analogies to twin particles sharing a connection, such that when you measure one, you instantly know the state of the other, can be used to simplify this idea. To accommodate for different educational backgrounds, other analogies closer to every-day life are provided in Table V .

IV-B 3 Measurement

The final foundational concept is measurement . In classical mechanics, looking at something does not change its state. However, in quantum mechanics, a qubit can be in a superposition of both 0 and 1 at the same time but when measured it must collapse to either 0 or 1. The quantum measurement collapse can be likened to trying to observe the natural behaviour of wild animals at night via flash photography. As you attempt to capture the positions of the animal herd, the sudden burst of light alters their natural behaviour and they freeze in a given position. This property, has applications for network security. If we send classical bits from one place to another, we have no way to know whether they were observed/eavesdropped by a malicious user. However, if we communicate using qubits, a malicious user who observes the qubits will collapse the wave function and we will know that the message was intercepted.

\ket{+} | start_ARG + end_ARG ⟩ or | − ⟩ ket \ket{-} | start_ARG - end_ARG ⟩ ? If we are measuring with respect to the | 0 ⟩ ket 0 \ket{0} | start_ARG 0 end_ARG ⟩ , | 1 ⟩ ket 1 \ket{1} | start_ARG 1 end_ARG ⟩ basis, we are asking which will it collapse to | 0 ⟩ ket 0 \ket{0} | start_ARG 0 end_ARG ⟩ or | 1 ⟩ ket 1 \ket{1} | start_ARG 1 end_ARG ⟩ ?

When we measure with respect to one basis all other bases (even if previously measured move back into a superposition state). Think of it like two clowns looking over each of your shoulders. When you turn to look at one clown, the other one starts changing its face and costume. You spin to look at this clown and now they freeze in position but the other one starts moving again. No matter how quickly you turn you can’t see both clowns at the same time. This phenomenon is at the core of the famous Heisenberg Uncertainty Principle.

To facilitate quantum computing access, quantum simulators can be used to demonstrate quantum computing concepts in a controlled environment and to allow students to experiment and visualize quantum processes.

When possible, access to cloud-based quantum computing platforms (e.g., Amazon Braket, IBM Quantum Experience, Microsoft Azure Quantum) facilitates hands-on experience for students to run quantum programs on actual quantum processors.

The students are encouraged to actively participate in discussions, solve problems, and collaborate on quantum projects. Interactive tutorials, quizzes, and assignments are used to reinforce learning.

One example or interactive learning is encouraging students to develop their own analogies for quantum concepts. This utilization of analogies not only enhances the comprehension of intricate scientific concepts but also plays a pivotal role in fostering creative thinking among students. Encouraging learners to explore complex ideas through analogies serves as a catalyst for their creative cognition. Analogies act as bridges, connecting unfamiliar or abstract concepts to relatable and tangible experiences [ 43 ] . When students are prompted to decipher scientific theories or abstract notions by drawing parallels to everyday phenomena, it sparks their imagination and ingenuity. This approach prompts them to think “out of the box”, fostering the ability to envision connections and solutions beyond conventional boundaries.

Moreover, involving students in the process of crafting and dissecting analogies cultivates their critical thinking and problem-solving skills [ 41 ] . By encouraging them to construct domain-specific analogies, educators empower students to exercise their creativity and analytical reasoning. Engaging in the creation of analogies requires students to discern fundamental characteristics and relationships between dissimilar concepts, honing their abilities to identify patterns and similarities. This practice not only aids in comprehending complex topics but also nurtures a mindset that values imaginative thinking and innovative problem-solving—an indispensable skill set for their academic and professional endeavors. Ultimately, encouraging the use and analysis of analogies stimulates students’ intellectual curiosity and encourages them to approach challenges with resourcefulness and adaptability [ 37 , 38 , 39 , 40 , 41 ] .

V Conclusions and Future Work

The layered learning approach proposed in this paper offers a structured and practical framework for teaching quantum computing concepts to computer science students. By emphasizing foundational knowledge and practical applications for quantum computing, we aim to address the challenge of teaching quantum computing to students unfamiliar with quantum mechanics. The use of good analogies has shown promise in teaching scientific subjects and we present an collection of analogies relevant to quantum computing to help bridge the gap between quantum computing and the “typical” computer science student.

Through the presented teaching approaches, students are introduced to quantum principles by first cementing their knowledge of classical foundations such as algorithms, data structures, and complexity theory before moving onto quantum foundations including qubits, quantum gates, superposition, and entanglement. The goal is to teach these quantum topics and provide computer science students an insight into the realm of quantum computing without the prerequisite of extensive quantum mechanics knowledge.

The paper underscores the importance of shifting educational focus to the benefits of quantum computing for real-world applications, demonstrating the potential of quantum computing in domains like cryptography and optimization.

V-A Future Work

Refinement of Analogies . We plan to further explore and refine domain-specific analogies that effectively illustrate complex quantum concepts to diverse student cohorts. This includes the development of new analogies based on students’ feedback and understanding; and, the use of our interdisciplinary network of quantum researchers to increase the diversity of domain-specific analogies.

Evaluation and Assessment. It is also necessary to conduct comprehensive assessments and evaluations to measure the effectiveness of the proposed teaching methodologies. This involves collecting data on student learning outcomes, engagement levels, and understanding through pre-and post-assessments.

Expanded Curriculum Development . We are currently pursuing the development of practical teaching materials for quantum computing and the enhancement of the curriculum to encompass quantum computing topics such as quantum error correction, quantum machine learning algorithms, and quantum cryptography.

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An Innovative Journey to Scalable Computer Science Programs

By abbie misha may 8, 2024.

Image Credit: Minecraft Education

In a time when technological advancements shape our daily lives and drive economic growth, focusing on STEM (science, technology, engineering and mathematics) education in K-12 schools is not just a trend but a necessity. Initiatives like the U.S. Department of Education's YOU Belong in STEM and the National Science Foundation's vision for the STEM Education of the Future underscore a national commitment to equipping students with the skills and knowledge needed to thrive in a tech-centric world, ensuring equitable access to opportunities that foster innovation and sustain the economy.

As the national spotlight illuminates the critical importance of STEM education, educators are tasked with translating these overarching goals into tangible experiences for students. Recently, EdSurge spoke with Valerie Brock , senior implementation manager at New York City’s Department of Education Computer Science for All (CS4All) , about her journey with STEM education.

EdSurge: What experiences laid the foundation for your role as a leader in STEM education in NYC Public Schools?

Brock: In 2017, after 10 years of teaching in NYC Public Schools, the largest school district in the country, I transitioned to an “out-of-classroom” position. I was tasked with providing reading intervention services for at-risk K-8 students. When my principal asked if I would be interested in teaching one elective period per day for the middle school population, I suggested a STEM elective since I had just taught a summer full of STEM during NYC’s annual STEM in the City programming.

Despite STEM being a relatively new terrain for me, I eagerly accepted the mission to ignite the curiosity and imagination of my students. I embraced plenty of dynamic, hands-on projects: harnessing the sun's power with homemade solar ovens, finding the magic of coding and assembling fidget spinners.

Minecraft Education ’s blocky world became the undisputed champion of engagement. Some of my coworkers taught the after-school program in the building and had already successfully integrated Minecraft into their STEM curriculum. Recognizing the students' enthusiasm for these pixelated realms, I experimented with it in my teaching practice.

My students were captivated by the game, and together, we crafted an unforgettable classroom experience and clinched victory in the annual holiday door decoration contest with a Minecraft masterpiece! Witnessing a classroom buzzing with excitement and brimming with knowledge was an educator's dream come true.

In 2018, my journey took a new turn as I stepped into the role of a computer science education manager, with a mission to sprinkle the seeds of meaningful computer science education across the vast educational landscape of NYC Public Schools. Since 2015, CS4All has worked diligently to ensure that all public school students in New York City learn computer science, emphasizing students who identify as girls, Black and LatinX. By 2021, 91 percent of schools in New York City offered computer science (up from 76 percent in 2019).

Then, in 2020, in the throes of a world turned upside down, where screens became windows to knowledge, we noticed a spark: Students, now with ample screen time, took to teaching themselves coding skills. Accessibility had always been the hurdle we couldn't leap — until the pandemic handed us the key.

With newfound access to Minecraft Education for every district student through our districtwide Microsoft 365 licenses, we seized the moment to launch professional learning experiences for educators, merging the beloved gaming experience with foundational computer science skills.

What plans did you implement to scale your approach?

Our collaboration with Minecraft Education experts was pivotal in designing an all-encompassing educational odyssey. Partnering with Insight 2 Execution (i2e) , highly skilled edtech consultants, connected us with nationwide experts in Minecraft Education. It was imperative to secure a facilitator who adeptly navigated Minecraft's digital landscapes and coding language. Additionally, we stressed the importance of educators having a solid grasp of computer science basics before delving into Minecraft. Ensuring the presence of an NYC Public Schools technical expert in every session guaranteed uninterrupted learning. To fortify educators' understanding of Minecraft, we introduced a virtual learning sequence starting with "Minecraft 101."

Since spring 2021, our journey has been exciting as we introduce upper elementary educators to the intersection of computer science and Minecraft Education. We quickly discovered the immense value of a meticulous approach: providing educators with a detailed agenda, a form to submit questions and concerns, and pre and post-exit tickets. These resources not only guide educators through the learning process but also enable us to gather feedback for ongoing improvement and immediate support.

Can you elaborate on some of these endeavors' outcomes and what you hope to see in future successes?

Our initiatives have flourished, with around 300 educators from approximately 250 NYC Public Schools becoming skilled on the Minecraft Education platform through our programs. This success has facilitated new collaborations, extending student benefits beyond initial expectations. In December 2023, we hosted our inaugural city-wide coding event, collaborating with Logics Academy , engaging students from over 400 NYC Public Schools in the Hour of Code: Generation AI event. Students explored the expansive possibilities of AI and learned about the significance of creating equitable and dependable technology. They tackled coding challenges, unraveled engaging puzzles and applied ethical AI concepts. Educators and students are still replaying the session in class as of today!

Principals from several elementary schools have reached out to me to ensure Minecraft Education is in their programming. Teachers have informed me that they are forming after-school and lunchtime coding clubs. Our city-wide Minecraft Education Battle of the Boroughs Challenge has reached new heights as well. For the first time, we received submissions from over 475 school teams, ranging from kindergarten to 12th grade students. And just recently, a teacher from Manhattan enthusiastically shared that his class of second graders is not only engrossed in Minecraft but is also learning to code.

As we look to the future of computer science education, our goal is to sustain and enhance our partnerships with external organizations, offering diverse and enriching experiences for both students and educators. We are also focused on expanding our internal offerings, encompassing professional development, instructional coaching and extensive support for teachers and school leaders. These initiatives aim to bolster the adoption and effectiveness of computer science education, beginning at the elementary level.

We eagerly anticipate leveraging Minecraft’s extensive AI-related activities to foster a comprehensive understanding of ethical AI among all students. We are excited about the advancements and innovations that await computer science education.

This article was sponsored by Minecraft Education and produced by the Solutions Studio team.

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Computer Science Students Design AI Applications for Research Computer Science Students Design AI Applications for Research

Bmbf-funded bntrainee project transferring expertise in modern machine-learning algorithms to other fields.

BNTrAinee, a project funded by the Federal Ministry of Education and Research (BMBF) and based at the University of Bonn, is developing AI-supported answers to specific research questions and is forging links between the University’s computer science teams and all manner of other subjects. This collaboration is now beginning to bear fruit, with computer science students joining forces with historians to create an algorithm that can help analyze old newspaper articles.

When we talk of artificial intelligence (AI), we mainly mean adaptive computer programs, i.e. those with the capacity to learn. These are trained on a vast pool of data and spot certain regularities within it. “Methods like these can make it easier to work through tasks that would otherwise be very time-consuming and labor-intensive,” explains Dr. Moritz Wolter, a computer scientist at the University of Bonn. “So there are a great many fields of research that could benefit enormously. We’re trying to help them out with our expertise.”

Wolter is one of the coordinators of the BNTrAinee project. Run by the Digital Science Center at the University of Bonn, it is geared toward driving forward this interdisciplinary collaboration and has secured an impressive €1.99 million in funding from the BMBF over the past two years. “The project is teaming up students with researchers who have a problem they want to solve using AI,” says the computer scientist, who is also a member of the Transdisciplinary Research Area (TRA) “Modelling” at the University of Bonn.

A win-win situation

Thinking outside the box in this way benefits both sides in equal measure. “Our students have to code software for their degree anyway,” Dr. Elena Trunz, also coordinator of BNTrAInee, says. “BNTrAinee allows them to do so as part of a real-life research project, with the added satisfaction that the fruits of their labor will actually get used afterward. In turn, the users—i.e. the researchers and their students—find out how they can apply AI and machine learning to their own projects to their advantage. At the same time, they see where their limits are.”

Both sides are also learning to speak a common language. The up-and-coming computer scientists first need to understand precisely what specific issue AI is supposed to help with, while their clients learn what data the algorithms require for this purpose and how it has to be structured. They are also given training in the mechanics of learnable methods, which covers some of the issues tackled recently by AI researchers, such as the question of what basis the methods use to draw their conclusions in the first place. This is because many algorithms are a kind of “black box”—they supply results, but it is unclear how they arrive at them. This makes it harder to gauge how reliably they are actually working.

How do financial crises influence “situation wanted” ads?

Someone who has high hopes of adaptive algorithms is Dr. Felix Selgert. One of the questions that the economic historian is investigating is how economic turmoil, such as that experienced during the hyperinflation of 1923, is reflected in the press. “For instance, I’m interested in what conclusions about the mood in society you can draw from newspaper articles,” he says. “My research is also focusing on analyzing ads, such as job and promotional ads.”

His problem is the sheer quantity of material that he would need to go through. At some points during the 1920s, for instance, the “Kölnische Zeitung” newspaper alone was being printed several times a day, 365 days a year. Even transcribing the editions from a single year, i.e. creating a digital copy, would mean thousands of hours’ work. “Traditional optical character recognition software offers little help with that, unfortunately,” he says. “It has massive problems with the layout, among other things.”

This is because paper was hard to come by in those days, so the newsprint was packed tightly on the page, and thin lines instead of blank space were used as column separators. “Normal” computer programs often miss these separators. “This causes them to amalgamate two articles that are next to each other, for instance,” Selgert says. They also have difficulties identifying headlines or subheadlines correctly and recognizing what story they belong to.

Computer science students have therefore developed an AI application that detects the layout of a page and splits it into its constituent elements. The next step will be to use another piece of self-learning software for character recognition, although this is still being developed. “The ultimate aim is for the AI to capture the full text of all articles and other elements in a scanned-in edition and categorize everything automatically,” adds Selgert, who is also a member of the Individuals and Societies Transdisciplinary Research Area at the University of Bonn. “But there’s a long way to go until we get to that point.”

Better cancer diagnosis, more data protection

The project is just one example of how AI algorithms are being tailored to some highly specific research questions. For instance, computer science students at the University and radiologists at the University Hospital Bonn want to help improve cancer diagnosis and are thus developing techniques for analyzing microscope images of removed tissue. Another project, meanwhile, is all about tightening data protection. Biologists often record animal sounds to get an idea of how biodiverse a particular area is. Sometimes, their recordings also capture “background noise” in the form of people’s conversations, so an algorithm is to be used to identify the relevant snippets and remove them automatically.

The University of Bonn’s Digital Strategy (www.digital.uni-bonn.de) sets out the measures and structures required for its digital transformation. The BNTrAinee project is an initiative forming part of the strategy’s Digital Skills set of objectives and aims to build AI expertise across discipline boundaries. Find out more about the Digital Strategy at https://www.digital.uni-bonn.de .

Further information on BNTrAInee: https://trainee.cs.uni-bonn.de/

Dr. Moritz Wolter High Performance Computing & Analytics Lab (HPC/A) University of Bonn Phone: +49 228 73 60938 E-Mail: [email protected]

Dr. Elena Trunz Institute for Computer Science II—Visual Computing University of Bonn Phone: +49 228 73-54191 Email: [email protected]

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Billionaire Frank McCourt's Project Liberty forms consortium to bid for TikTok

Frank McCourt, majority shareholder of the French soccer team Marseille, attends a game in Paris on Oct. 16, 2022.

Entrepreneur and former Los Angeles Dodgers owner Frank McCourt said on Wednesday his organization, Project Liberty, is forming a consortium to buy social media platform TikTok in the United States.

A law signed by President Joe Biden on April 24 gives the social media platform’s owner, ByteDance, until Jan. 19 next year to sell TikTok or face a ban.

The bill was passed by U.S. lawmakers on account of worries that China could access Americans’ data or surveil them through the app.

The White House had said it wants to see Chinese-based ownership ended on national security grounds but not a ban on TikTok.

Project Liberty, working with Guggenheim Securities, law firm Kirkland & Ellis, technologies, academics and others, proposed to migrate the platform to a digital open-source protocol.

The inventor of the World Wide Web, Sir Tim Berners-Lee, and David Clark, a senior research scientist at the MIT Computer Science and Artificial Intelligence Laboratory, are among the supporters of the bid, the organization said.

Project Liberty had launched the open-source Decentralized Social Networking Protocol in 2021, establishing a shared social graph that is not dependent on a specific application or a centralized platform.

The organization encompasses work of the Project Liberty Institute, with an international partner network that includes academic institutions and a for-profit arm that includes a technology team developing digital infrastructure.

Unique Program Offers Campus Research Opportunities for Online Students

From her home more than 800 miles away, Georgia Tech online master's student Jasmine Tata is monitoring fish in aquariums at Georgia Tech.

Tata is a New York-based QA analyst and project manager. She started the Online Master of Science in Computer Science (OMSCS) program in Fall 2022 and joined FishStalkers last year.

The student-led research program is part of the School of Biological Sciences' McGrath Lab . Its researchers use machine learning, computer vision, and other technologies to better understand the evolution of animal behaviors.

One of the lab's research projects studies Lake Malawi cichlids to explore connections between observed behavior and brain function.

The FishStalkers are vital to the project. They collect video, depth, and other data from individual fish using Raspberry Pi single-board computers. This information, coupled with open-source code they developed, allows the group to track, monitor, and classify the behaviors of a fish as it builds and maintains its bower, which is a sand structure these cichlids use to attract mates.

Along with monitoring the research tanks, Tata's contributions include improving the automated collection and analysis of data streaming from the Pis. She's also helping to adapt the data pipeline to work with yellow-head, orange-cap, and other cichlid species.

[RELATED: Georgia Tech's OMSCS Program Celebrates 10th Anniversary]

"I've enjoyed learning more about new problems in a relatively unfamiliar field. In a pure computer science-focused lab, I never would experience the frustrations of data collection that come with biological subjects," said Tata.

"The fish builds bowers on its own schedule, and data collection must accurately capture this, regardless of weekends or holidays."

Tata says her experience with FishStalkers has given her new ideas about presenting data to non-technical team members. The team uses a spreadsheet integrated with data collection scripts running on the Raspberry Pis. The spreadsheet allows someone without technical knowledge to pause, upload data, or start new trials simply by toggling a dropdown.

"This has given me a lot of ideas about how to meet people where they are in terms of technical skills when it comes to user interface design and has encouraged me to learn more about human-computer interaction ," said Tata.

Tata learned about the FishStalkers research group when its founder, Breanna Shi , reached out through the OMSCS Slack study channel. Shi developed the group through Georgia Tech's Vertically Integrated Projects (VIP) program as a mentorship program.

"Given their real-world computer science experience, I wanted to see if there were OMSCS students interested in collaborating on FishStalkers projects and assisting in the mentorship of undergraduate researchers," said Shi.

Shi is a third-year Ph.D. student studying bioinformatics with minors in machine learning and higher education. She created FishStalkers as a mentorship program because she recognized that undergraduate and masters-level students could feel less valued or isolated in research environments.

"The FishStalkers model empowers all its researchers with the respect and responsibility as a full team member. Whether it's your first week as a FishStalker or your last, you will complete tasks that benefit the research team and yourself," said Shi.

[RELATED: Women-Centered Mentorship Provides Empowerment to Conquer Ph.D.]

Tata's experience in the business world made her a good fit for the FishStalkers program. Shi says Tata contributes valuable insight to the group as a mentor because most students approach the program from a purely academic viewpoint.

"Jasmine, like other OMSCS students, works full-time and attends the OMSCS program part-time. Her roles as a project manager and a software QA analyst allow her to contribute a unique perspective to the FishStalkers group," said Shi.

In addition to sharing her experience mentoring two OMSCS students this semester, Tata has helped Shi overcome some of the inherent challenges of long-distance collaboration. These include creating a sense of interpersonal connection among in-person and remote research team members.

Group meetings host a virtual link to enhance the online research experience. Every member provides progress updates during the sessions. The researchers also virtually check in and out of their research hours in a shared group chat and describe the work completed during their check-out.

"FishStalkers also runs a monthly lab-buddy program where a researcher is paired with a new buddy each month to schedule a 30-minute meeting to chat and learn about each other's work," said Shi.

"These strategies benefit OMSCS students in our group and provide a positive research environment for junior researchers. We seek to incorporate innovative strategies to create an accessible research environment for all students interested in participating in our research," said Shi.

FishStalkers has been such a success that Shi is expanding the model. This fall, Shi will work with OMSCS Executive Director David Joyner and OMSCS Associate Director of Research Nick Lytle to connect OMSCS students with interdisciplinary research projects in labs across campus.

"My role will be to establish relationships between data collectors and data analyzers to provide a service to non-technical labs across campus and a valuable research experience for OMSCS students," said Shi.

"We will be building from my existing work in image processing in the McGrath Lab and expanding to other labs with data analysis needs. I am very excited to have the experience of growing as a collaborator."

Ben Snedeker, Communications Manager Georgia Tech College of Computing