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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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TYPES OF RESEARCH IN COMPUTING SCIENCE SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE

1. background (and updates).

A Research Strategy conference was organised by the CPHC (UK Conference of Professors and Heads of Computer Science) at the University of Manchester 6-7 Jan 2000. It was attended by about 100(?) people (not only professors and heads, including some researchers in industry). On the first afternoon there was an introductory panel session concerned with how the Computing Science community should present its research objectives and achievements to EPSRC and the bodies which award funding to EPSRC. During the ensuing discussion I suggested a high level way of dividing up research aims into four main categories (later expanded to five), which, in part, need to be evaluated differently. Both during the conference and subsequently I received comments and requests for clarification and references. So I thought I should write down what I had said, expand it a bit, and circulate it for comment and criticism. The resulting document is in this file at http://www.cs.bham.ac.uk/research/projects/cogaff/misc/cs-research.html [1] Whenever I have afterthoughts, or receive criticisms, comments and suggestions for improvements, I may modify/correct/extend the file, with acknowledgements where appropriate in the notes at the end. NOTE 17 Aug 2016 The viewpoint expressed here (and by others at the conference in 2000) is inconsistent with the slogan (by Fred Brooks) highlighted below, claiming that computer science is an engineering discipline. The ever increasing overlap between CS and other disciplines, going far beyond provision of tools, is evidence that Brooks had a blinkered view of CS. He is not alone. He wrote: "Perhaps the most pertinent distinction is that between scientific and engineering disciplines. That distinction lies not so much in the activities of the practitioners as in their purposes. A high-energy physicist may easily spend most of his time building his apparatus; a spacecraft engineer may easily spend most of his time studying the behavior of materials in vacuum. Nevertheless, the scientist builds in order to study; the engineer studies in order to build. " ....... "In a word, the computer scientist is a toolsmith--no more, but no less. It is an honorable calling." F.P. Brooks, Jr., The Computer Scientist As Toolsmith, in Communications of The Acm , March 1996/Vol. 39, No. 3 ACM award acceptance lecture delivered at SIGGRAPH 94. http://www.cs.unc.edu/~brooks/Toolsmith-CACM.pdf

NOTE added 18 Dec 2007

A paper written by Allen Newell addresses some of the issues listed here. A. Newell (1983) Intellectual issues in the history of artificial intelligence, in The study of information: interdisciplinary messages pp. 187--227, Eds. F. Machlup and U. Mansfield, John Wiley \& Sons, New York, Available in the Newell Archives http://diva.library.cmu.edu/webapp/newell/item.jsp?q=box00034/fld02334/bdl0002/doc0001/

NOTE added 2 Nov 2007

Alan Bundy has developed a web site which serves some of the same purposes as this one, here http://www.inf.ed.ac.uk/teaching/courses/irm/notes/hypotheses.html The Need for Hypotheses in Informatics

NOTE added 28 Feb 2006

Most of this document was written before the UKCRC initiative on Research Grand challenges . Several of the grand challenge proposals that emerged within that initiative are examples of the view reported in this document. They presented computing research problems beyond the scope of "traditional" computer science, especially GC1: In Vivo -- In Silico GC5: The Architecture of Brain & Mind GC7: Journeys in Nonclassical Computation

NOTE Added 19 Dec 2004

The UKCRC Grand Challenge initiative proposed in 2003, illustrates some of the points made below about different kinds of research. Discussions of Grand Challenge 5 ('Architecture of Brain and Mind'), which is one of the long term grand challenges with no definite end point (like many scientific and medical grand challenges), raised the difficult question of how to identify progress. This is an issue addressed in a very relevant way in the writings of Imre Lakatos, who extended some of the ideas of Karl Popper by making a distinction between 'progressive' and 'degenerating' research programmes, where the important point is that it may be impossible to decide whether a research programme is of one type or another at early stages in the programme: the decision requires analysis of an extended period of research. There are many internet sites discussing, summarising, criticising or reproducing Lakatos papers. A very short summary of his ideas can be found here . A slightly longer summary can be found here . In the context of Grand Challenge 5 I offered a scenario-based methodology that is useful both for planning research and for evaluating it, based on development of a large collection of (partially) ordered scenarios of varying depth and difficulty. The methodology is summarised here . It is also being used in connection with an ambitious EU-funded project that began in September 2004.

NOTE Added 9 Jan 2001

Following recent discussions about UK CS research on the cphc-members email list, and a note circulated by Alan Bundy referring to a list of research topics produced some time ago by a CPHC committee [2] , I have added a new category of research topics, "Research on Social and Economic Issues". So although there were originally four categories, there are now five, although the new one is not a sub-discipline of Computer Science but rather a multi-disciplinary research area, with a large component of Computer Science.

2. The Five Categories of Research in CS and AI

Research in Computing Science and AI falls into four main categories, with different types of aims, and different success/failure criteria, though each of the categories feeds on and contributes to the others, and there are some kinds of research which straddle categories. There is a fifth cross disciplinary category which is of great interest to many computer scientists though it is not strictly a part of Computer Science or AI, though concepts and techniques from both form part of its subject matter and can also be used to further its aims. The study of what is possible -- and its scope and limits Including both mathematical and less formal modes of theorising. [3] The study of existing (naturally occurring) information-processing systems E.g. animals, societies, brains, minds, .... Sometimes described as "Natural computation". Research involving creation of new useful information-processing systems [4] I.e. research directly related to engineering applications. The creation and evaluation of tools, formalisms and techniques to support all these activities. Research on social and economic issues Including studies of the social and economic impact of computing and AI, ethical issues, changing views of humanity, etc. These categories are described in more detail below. Because different kinds of activity need to be evaluated in different ways (see below), there are implications regarding how EPSRC ought to organise its reviewing of grant proposals, and perhaps also implications regarding what proposers should say about their objectives. In particular, we should strongly resist real or imagined pressures to force all our research into Category 3, and should not be tempted to disguise research in the other categories, or justify it merely as a contribution to Category 3. Added 30 Mar 2005: In the light of recent discussions on the CPHC email list it may be worth subdividing this category in various ways. E.g. some of the research contributions to practical applications involved a relatively simple yet new and powerful key idea (e.g. the original idea of the World Wide Web), whereas others are inherently concerned with production of something large and complex requiring the development of a large and complex collection of ideas, e.g. the design of a secure and robust air traffic control system, or a novel nationwide information system for the health service. Many such systems require the use of knowledge and techniques from many disciplines. The next section explains in more detail what the above categories are, and how they are related and mutually dependent. The section after that explains how the evaluation criteria relevant to these categories of research differ and where they overlap. (In what follows I use "type" and "category" interchangeably as terms of ordinary English, not as technical terms.) NB: where lists of examples are given they are merely illustrative and are not intended to be exhaustive, or to define a category.

3. Comments on the categories

3.1. The study of what is possible -- and its scope and limits This includes a lot of work using mathematics and logic, such as work on semantics of computation, and theorems relating to limits of computation, complexity, properties of mechanisms for cryptography, mathematical analysis of different classes of computations, studies of the expressive power of different formalisms, analysis of properties of various kinds of information-processing architectures, network protocols, scheduling algorithms, etc. etc. Much of this work involves the study of types of virtual machines and their properties. They need not be machines which could exist in nature: e.g. some might be infinite machines. This category also includes less formal, and possibly less rigorous [3] , exploratory investigations of new types of architectures, including virtual machine architectures, hardware and software mechanisms, forms of communication, ontologies, etc. in order to investigate their properties and their trade-offs. Examples in AI include explorations of various forms of representation or high level architectures for use in intelligent systems. Sometimes work that starts off in this informal way leads to new formal, mathematical developments, as has happened throughout the history of mathematics. Often work in Category 1 builds on and abstracts from experience gained in tasks in the other categories, just as much of mathematics derives from attempts to find good ways of modelling complex physical structures and processes, e.g. Newton's and Leibniz' invention of Calculus, and the early work on probability theory inspired by gambling devices. Very often this theoretical work addresses problems that are sufficiently complex to require the use of tools of the sorts developed in research of Type 4. Purely theoretical work often develops in such a way as to provide concepts, models, theorems and techniques relevant to the other three kinds of research, though even if it does not do so it can still be of great interest and worth doing as a contribution to human knowledge. It has intrinsic value comparable to that of music, poetry, painting, sculpture, literature, mathematics and dare I say philosophy.

3.2. The study of existing (naturally occurring) information-processing systems

(Sometimes described as "Natural computation".) This is scientific research of another kind: the attempt to understand, explain or model, things that exist in the world, as opposed to exploring what is possible (Category 1) or finding ways of creating new useful things (Category 3). Of course such understanding can sometimes lead to useful practical applications, by enabling us to predict, control or modify some of the behaviour of systems after we understand them. But that is not a requirement for the work to be of great scientific value (though it can be part of the selection process where there are competing theories). There are many kinds of naturally occurring systems, including machines that manipulate matter, machines that manipulate forces and energy and machines that manipulate information -- including virtual machines that cannot be observed and measured as physical machines can. Long before there were computers or computer science there were many types of extremely sophisticated information-processing systems, including animal brains, insect colonies, animal societies, human social and economic systems, business organisations, etc. More recently new systems have grown which are enabled by information-processing artefacts, but are as much natural systems worthy of study as a society or the weather, for instance traffic systems or the internet. The processes of biological evolution form another such naturally occurring information-processing system. Over huge timescales, using mechanisms which are still only partially understood, it compiles information about many types of environments and many kinds of tasks (e.g. serving needs of organisms) into a diverse collection of wonderfully complex and extremely successful designs for working systems, far exceeding in complexity, sophistication and amazing robustness, anything yet produced by human designers of information-processing machines. Some physicists argue that even the physical universe is best construed as ultimately composed of information-processing systems, not yet fully understood. Whether work in computing science will contribute to that understanding I do not know, though there are attempts in that direction. Prior to the development of computing science the study of complex naturally occurring information-processing systems was often very shallow, mostly just empirical data-collection, often using theories expressed only in crude general forms or coarse-grained equations or statistical correlations which failed to capture or explain any of the intricate detail of processes observed. Since the middle of the last century, the study of different forms computation has enriched our ability to find new ways of formulating and testing powerful models and theories for explaining and predicting natural phenomena. Information-processing models and theories are being developed in many scientific domains, as people find that they provide richer, more powerful explanatory capabilities than the old paradigms (e.g. equations relating observed of measurable quantities). This in turn is feeding new ideas into computing science. This has most obviously happened over the last 50 years or so in work in Artificial Intelligence, a discipline whose scientific "arm" has in the past mainly focused on attempts to model and explain aspects of human-intelligence, though there are increasingly attempts at modelling various kinds of animal intelligence. (See the overview of AI in http://www.cs.bham.ac.uk/~axs/courses/ai.html .) Unfortunately many psychologists have no appreciation of this as shown by the pressures by which the British Psychological Society causes Psychology departments to stop allowing their students to take AI courses, which are not recognised as relevant. (Behind all that is an out-dated philosophy of science based on an incorrect model of physics as a science that collects lots of measurements and then searches for correlations.) A more recent development is the growing interest in interpreting biological evolution as a form of information-processing which has also inspired exploration of novel forms of computation which may or may not turn out to be useful for modelling nature. It is arguable that the activity of engineers, working individually or in teams, is an example of a naturally occurring process and therefore empirical investigations of different kinds of practices, methodologies languages, tools etc., and how they work, could fit into Category 2. This is usually an intrinsic part of research in Category 4, which is primarily intended to support Category 3. However, analysis and simulation of human engineering activities can fit into Category 2, and work in AI/Cognitive science on simulation of human design processes would clearly do so. [5]

3.3. Research involving creation of new useful information-processing systems. [4]

Research closely related to production, analysis and evaluation of practical applications is the main engineering branch of computing science, though Category 4 also includes a type of engineering. Category 3 overlaps with Category 2 insofar creation of explanatory theories and models often involves designing and implementing new and complex systems requiring significant engineering skills. There is also overlap insofar as building useful devices often requires a deep understanding of the environment in which they are to operate. E.g. many software engineering projects producing systems to be used by or interact with humans, including HCI projects, have failed because they used shallow and grossly inadequate models of human cognition, motivation, learning, etc. Despite some overlap with Categories 1 and 2, the primary goal of research in Category 3 is not to study theoretically possible systems and their properties, nor to help us understand already occurring information-processing systems. The goal is to enable us to create new practically useful systems, which may either: (a) provide new (or improved) types of artefacts capable of performing functions that were previously performed only by natural systems such as humans and other animals (e.g. doing numerical computations, proving mathematical theorems, translating from one language to another, designing new machines, managing office records, recognising faces) or, increasingly often, (b) develop systems to perform tasks that could not be achieved at all previously, e.g. the construction of global communication networks, accurately forecasting the weather, controlling extremely complex machines and factories, safely giving trainee pilots experience of flying an airbus without leaving the ground, etc. However for this to count as research it must also increase knowledge . If it merely uses existing computing knowledge to produce new tools that are useful to increase knowledge about some other domain (e.g. physics, biology, etc.) that may make it research in the other discipline. If it increases our explicit re-usable knowledge about how to specify, design, build, test, maintain, improve, or evaluate information-processing systems then it is research in the field of software or computer engineering, or AI engineering. (This is not intended to be a precise definition: there may not be one.) Scientific and engineering research work in Category 3 can be contrasted with a great deal of system development activity that may be of practical use, but either (i) directly deploys existing knowledge in standard ways without extending that knowledge, or (ii) depends only on the intuitive, often unarticulated, grasp of what does and does not work. As regards (ii), unarticulated intuitive knowledge and skills gained through practical experience (perhaps combined with natural gifts), may be called craft since it does not require the use and development of explicit theories about what does and does not work and why (the result of research of Types 1 and 2). Even when such craft work extends what we can do, it is not in itself research and should not be treated or evaluated as such, though it may be a precursor to important research. It may produce useful results but does not, in the process advance communicable knowledge. However craft in building computing systems, like many other types of craft, can, and often does, later stimulate more explicit science and engineering: we often first discover that we can do something, then later wonder how and seek explanations. [6] The resulting articulation leads us to understand precisely what was achieved, the conditions under which it can be achieved, how it can be controlled, varied, extended, etc.

3.4. The creation and evaluation of tools, formalisms and techniques to support these activities

Category 4 can be seen as a subset of Category 3, though it may be useful to separate it out because its engineering goals are concerned with the processes of performing the tasks in the previous categories (and this category) and to that extent involves the pursuit of goals which could not have existed but for the existence of computing science. (That's only an approximate truth!) This category involves a diverse range of activities, including designing new programming languages, new formalisms for expressing requirements, compilers, tools for validating or checking programs or other specifications, tools for designing new computing hardware or checking hardware designs, automatic program synthesizers, tools to support exploratory design of software (e.g. most AI development environments) and many more. Research on design, analysis and testing methodologies, as well as tools to support them, can be included in this category, though it overlaps with other categories. [7] The design and production of new general purpose computers, compilers, operating systems, high level languages, graphical and other interaction devices and many more, clearly falls into both the third and fourth categories. Moreover, many tools which are initially of Type 4 can migrate into tools of Type 3, e.g. early AI software development tools which were later expanded into expert system shells. However, it is possible for a tool of Type 4 to have no obvious use outside computing science and yet be of great value. Perhaps an example might be a tool for automatic analysis and checking of the type-structure of a complex formula in a language used only by theorists, or a tool for analysing the structures of complex ontologies developed entirely for research purposes.

3.5. Research on social and economic issues

Research in this category normally requires collaboration with researchers from other disciplines such as psychology, sociology, anthropology, economics, law, management science, political science and philosophy. It includes attempting to understand all the various ways in which developments in computing technology and artificial intelligence have influenced social, educational, economic, legal and political processes and structures, and ways in which they may influence such processes in the future. It can also include exposing and analysing ethical implications, including the implications of the impact of the new technology on opportunities, resources, jobs, power structures, etc. for various social groups within countries and also the impact on international relations and relative power of nations, international companies, etc. It can also include analysis of ethical implications of views of the human mind arising out of developments in AI.

4. Evaluation Criteria for the above types of research

The five types of research have different evaluation criteria, though there is partial overlap. It is possible that the differences are not fully understood, either by politicians and civil servants who are concerned with funding decisions, or by some of the referees who comment on grant proposals. In particular where the research is concerned with testing or developing explanatory or predictive theories, the history of science shows that there can be rival theories which are both partially successful and both better than other theories attempting to explain the same phenomena, without there being any decisive way of telling which theory is better, at any particular time. However, As Imre Lakatos showed in I. Lakatos (1980), The methodology of scientific research programmes, in Philosophical papers, Vol I, Eds. J. Worrall and G. Currie, Cambridge University Press,

A. Sloman, (1978) The Computer Revolution in Philosophy, Philosophy, Science and Models of Mind, Harvester Press (and Humanities Press), Online here http://www.cs.bham.ac.uk/research/cogaff/crp

4.1 The study of what is possible -- and its scope and limits

The criteria for evaluation of this kind of research are subtle, unobvious, and closely related to criteria for evaluation of research in mathematics, logic, philosophy, theoretical physics, theoretical biology, etc. They involve notions like "depth", "power", "generality", "elegance", "difficulty", "potential applicability", "relevance to other problems", "synthesis", "integration", "opening up new research fields", etc. It can be very hard for some people who have not done this kind of research to appreciate its value. But there are plenty of widely referenced examples, e.g. Turing's invention of the notion of a Turing machine and his and Goedel's work on limit theorems and (less widely known) McCarthy's invention of a programming language that can operate on expressions in the language -- Lisp. (Alas, many developers of programming languages since then have ignored this idea!) It often turns out that new theories about what is possible also have enormous practical applications, though sometimes these are not understood, or deployable, until many years later. Many deep theoretical advances have had unexpected practical applications after considerable delay. E.g. the problem to be solved may not turn up for a long time, or the application may require additional developments which take a long time: most of the practical deployment of ideas about forms of information-processing had to wait for advances in physics, materials science, manufacturing technology, etc. to produce computers with the power, weight, size, price and diversity of uses that we know today. Because research of Type 1 is so hard to evaluate and of such potential importance, it may be necessary to devise mechanisms to keep it going and to keep diversifying it with minimal concern for evaluation by generally agreed criteria. (Compare the use of stochastic search mechanisms to solve really hard problems!)

4.2. The study of existing information-processing systems

Here the criteria for evaluation are more like those in empirical sciences, like experimental physics, biology, psychology, etc. The theories have to be tested against the facts. This can sometimes be done by using the theories to make predictions about behaviour of naturally occurring systems, or by showing how large numbers of different previously observed phenomena can be uniformly explained. Sometimes theories about natural information-processing mechanisms can be confirmed or disconfirmed by evidence gained by opening up the physical system or by sophisticated non-invasive techniques for observing internal processes (e.g. fmri scanners). Often however empirical testing is extremely difficult and has to be indirect, especially when the theory relates to a very complex virtual machine whose structure does not relate in any simple way to the underlying physical machinery, or where the complexity of the physical or physiological mechanisms makes de-compiling an intractable task. In that case theories may inevitably remain highly conjectural, making it hard to choose between rival alternatives with similar behaviour consequences. Sometimes this leads to sterile debates that would be better postponed until there is a better basis for choosing, while work in the rival camps continues to be supported. Often rival theories cannot be properly compared until long after they are first proposed. Sometimes, choosing between alternative theories requires introducing very indirect evidence: e.g. showing that the mechanisms of evolution could have produced one sort of architecture but not another, in order to rule out the second as a correct theory of how a human mind works. But truth is not enough for an explanatory theory to be valuable, for there are trivial or shallow truths: again notions like "depth", "generality", "explanatory power", "elegance", and a theory's ability to open up new research problems, are relevant to the evaluation of the theory as a contribution to science. All this is just a special case of philosophy of science, though most philosophers of science are unaware of the special complexities of scientific theories about information-processing systems, because they were brought up to philosophise about simpler sciences such as physics!

4.3. Research involving creation of new useful information-processing systems.

This sort of work has two kinds of criteria for evaluation: how well it extends knowledge and how useful the results are. Often the work. involves both producing new developments of Category 1 or 2 and also deploying them in creating something useful, e.g. exploring ideas about forms of computation, and then later building usable physical implementations of those ideas, or finding a deep explanation of certain diseases then using that explanation in the search for a cure. The two kinds of work need not proceed in that order: in some cases the practical results and explanatory theories may be developed in parallel, or practical difficulties in applying old ideas may point to the need to improve existing theories, formalisms, conceptual frameworks, etc. In all these cases the criteria for work of Category 1 or 2 are relevant to evaluating work in Category 3 because the work is composite in nature. But there is also evaluation of usefulness of new systems. However, usefulness has its own rewards (e.g. financial rewards) and unless there is also some advance in knowledge it is not research. This must be remembered in evaluating such projects in a research context. Not everyone will agree on criteria to be used in evaluating practical applications. Most people would agree that results can be evaluated in terms of benefits they bring in enhancing quality of life including new forms of entertainment, or facilitating other activities with important practical goals, e.g. preventing air traffic collisions, allowing secure transmission of confidential messages, or automatically diagnosing skin cancer at a very early stage, or designing a better tool for teaching mathematics. But some people will regard work that builds more powerful weapons that can bring death and destruction (euphemistically named "defence") as valuable whereas others will condemn such applications. Recent debates about genetically modified food illustrate this point. Moreover, as any Which? report shows evaluation can often be multi-dimensional with at best a partial ordering of the options available. In addition to the evaluation of the costs and benefits of new applicable systems, they can also sometimes be evaluated intrinsically , e.g. in terms of how elegant they are, how difficult they were to achieve, how ingenious or original their creators had to be. Some railway steam engines were beautiful as well as being powerful and fast, and some very useful bridges are also works of art. Lisp (the original version) and Prolog both have a type of beautiful simplicity in relation to their power as programming formalisms, unlike several others I dare not name. Those who attempt to convey a sense of style when teaching programming appreciate this point, apart from the fact that a good style can also have practical consequences, such as maintainability and re-usability.

4.4. The creation of tools, formalisms and techniques

These things can be evaluated both according to how well they facilitate work in the other three categories, and also according to the previously mentioned criteria which are independent of usefulness. Of course producing good tools for doing other things (e.g. for designing and testing models, for building applications, etc.) can be thought of simply as part of those other activities, and evaluated in relation to their indirect benefits. But the good ones have a kind of generality and power that is of value independently of the particular uses to which they are put. It could be argued that this fourth category is spurious: it should be lumped in as part of the third category, sharing its evaluation criteria. At first I was tempted to do this. However, the development of computing both as science and as engineering has depended on a remarkable amount of bootstrapping, where the most important applications of many tools, concepts, formalisms and techniques are the processes of producing more of the same. A spectacular example is the role of previous generations of hardware and software in producing each new generation of smaller, faster, cheaper, more powerful, computers.

4.5. Research on social and economic issues

The evaluation of research in this area is a huge topic beyond the scope of this note. However it links up with criteria for evaluating research in all the other disciplines involved in this research, including psychology, sociology, anthropology, economics, law, management science, political science and philosophy. In some cases there are significant disputes about how to evaluate research in these fields and the relevance of those disputes is likely to be inherited by research in this category.

[1] The ideas here overlap with those that went into the overview of AI which was produced (with help from colleagues in various places) for the QAA computing science benchmarking panel: http://www.cs.bham.ac.uk/~axs/courses/ai.html [2] Note Alan Bundy has a collection of papers by various authors on "Generic Questions" relating to CS: https://sweb.inf.ed.ac.uk/bundy/Generic_Questions/Generic%20Questions.html (Links restored 24 Aug 2016) [3] I am grateful to Jim Doran for reminding me of the need to allow less mathematical work in this category, especially as it applies to most of my work as a philosopher doing AI! [4] Michael Kay, ICL, pointed out that my original title for the third kind of research ("Creation of new useful information-processing systems") was misleading. Many people work on creating new useful information-processing systems but are not doing research. The description in section 3.3 was rephrased to accommodate his comments. Rachel Harrison, Reading University, suggested including evaluation in this category. [5] Rachel Harrison drew my attention to this point. [6] It may be that the only way to produce excellent engineers is to start by making them expert craftsmen and women! [7] Tom Addis, at Portsmouth University, pointed out in response to the first draft that I had not said anything about research on design and development methodologies. I have now placed this in Category 4, though some aspects of this work clearly belong in other categories, e.g. exploration of possible methodologies and modelling of human designers. Rachel Harrison pointed out that besides design methodologies there are also analysis and testing methodologies. I have grouped research on all of these together as supporting research of the other types. However, this can also be seen as an aspect of Category 3.

Research Techniques for Computer Science, Information Systems and Cybersecurity

• © 2023
• Uche M. Mbanaso 0 ,
• Lucienne Abrahams 1 ,
• Kennedy Chinedu Okafor 2

Centre for Cybersecurity Studies, Nasarawa State University, Keffi, Nigeria

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LINK Centre, University of the Witwatersrand, Johannesburg, South Africa

Department of mechatronics engineering, federal university of technology, owerri, nigeria.

• Provides a roadmap for CS, information systems and cybersecurity grappling with framing research topics
• Presents path for embarking on research projects by reducing complexities to understanding the topics’ relevant needs
• Distinguishes CS information systems and cybersecurity research while highlighting their intersection in a practical way

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Introduction

Retrospect and prospect: information systems research in the last and next 25 years

Data Mining Methodology in Support of a Systematic Review of Human Aspects of Cybersecurity

• Cybersecurity
• information systems
• contemporary research
• mind mapping
• funnel strategy
• quantitative research
• qualitative research

Table of contents (8 chapters)

Front matter, twenty-first century postgraduate research.

• Uche M. Mbanaso, Lucienne Abrahams, Kennedy Chinedu Okafor

Computer Science (CS), Information Systems (IS) and Cybersecurity (CY) Research

Designing the research proposal or interim report, adopting a funnel strategy and using mind mapping to visualize the research design, foundational research writing, background discussion and literature review for cs, is and cy, research philosophy, design and methodology, data collection, presentation and analysis, research management: starting, completing and submitting the final research report, dissertation or thesis, back matter, authors and affiliations.

Uche M. Mbanaso

Lucienne Abrahams

Kennedy Chinedu Okafor

About the authors

Uche M. Mbanaso (PhD) is a leading Cybersecurity subject matter expert (SME), and currently the Executive Director, Centre for Cyberspace Studies, an Associate Professor, Cybersecurity and Computing at Nasarawa State University, Keffi, Nigeria, a visiting academic at the LINK Centre, Wits University of Witwatersrand, Johannesburg, South Africa. He lectures computing and cybersecurity and has extensive experience in conducting research, having secured several grants for research studies. He remains a key player in the development of Cybersecurity, having played important roles in the development of Cybersecurity Policy and Strategy, Data and Privacy Protection, and many other Cybersecurity initiatives in Africa.

Luci Abrahams (PhD) is Director of the LINK Centre at Wits University, building research on digital innovation and how digital technologies and processes influence change. Studies include case studies in digital governance; digital skills gap analysis; digital strategy; scaling up innovation in tech hubs; open access in scholarly publishing (Open AIR research partnership); and health e-services improvement (Egypt-South Africa research partnership). Luci convenes the MA and PhD programmes in Interdisciplinary Digital Knowledge Economy Studies and supervises postgraduate research; lectures on short courses in disruptive technologies, digital operations, and leadership; and lectures on research methods for cybersecurity professional practice.

Kennedy Chinedu Okafor is a Senior Member, IEEE, USA; Chair of IEEE Consultants Network AG-Nigeria, and a Senior teaching researcher with the Department of Mechatronics Engineering, Federal University of Technology, Owerri-Nigeria. Kennedy is a World bank Faculty at the AFRICA Center of Excellence for Sustainable Power and Energy Development (ACESPED), University of Nigeria Nsukka (UNN), Nigeria. In 2017, Kennedy received the prestigious Vice-Chancellor Award as the overall best graduating Ph. D candidate from the Faculty of Engineering, UNN. He is a Senior research associate with the University of Johannesburg and a visiting Fellow at Imperial College London. Kennedy is an expert in Smart Cyberphysical systems, and Network Security within Mechatronics sub-specialty.

Bibliographic Information

Book Title : Research Techniques for Computer Science, Information Systems and Cybersecurity

Authors : Uche M. Mbanaso, Lucienne Abrahams, Kennedy Chinedu Okafor

DOI : https://doi.org/10.1007/978-3-031-30031-8

Publisher : Springer Cham

eBook Packages : Engineering , Engineering (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

Hardcover ISBN : 978-3-031-30030-1 Published: 25 May 2023

Softcover ISBN : 978-3-031-30033-2 Published: 26 May 2024

eBook ISBN : 978-3-031-30031-8 Published: 24 May 2023

Edition Number : 1

Number of Pages : XXXV, 161

Number of Illustrations : 23 b/w illustrations, 22 illustrations in colour

Topics : Engineering Design , Operations Research/Decision Theory , Research Skills , Business and Management, general

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Who are the most well-known computer scientists?

Is computer science used in video games, how do i learn computer science.

computer science

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What is computer science?

Computer science is the study of computers and computing as well as their theoretical and practical applications. Computer science applies the principles of mathematics , engineering , and logic to a plethora of functions, including algorithm formulation, software and hardware development, and artificial intelligence .

The most influential computer scientists include Alan Turing , the World War II code breaker commonly regarded as the “father of modern computing”; Tim Berners-Lee , inventor of the World Wide Web ; John McCarthy , inventor of the programming language LISP and artificial intelligence pioneer; and Grace Hopper , U.S. Navy officer and a key figure in the development of early computers such as the UNIVAC I as well as the development of the computer language compiler .

What can you do with computer science?

Computer science is applied to a wide range of disciplines that include modeling simulations such as the impacts of climate change and the Ebola virus , creating art and visualization through graphics rendering, and simulating a human interface through artificial intelligence and machine learning .

Video game development is grounded in the principles of computer science and programming . Modern graphics rendering in video games often employs advanced techniques such as ray tracing to provide realistic effects. The development of augmented reality and virtual reality has also expanded the range of possibilities of video game development.

Many universities across the world offer degrees that teach students the basics of computer science theory and the applications of computer programming . Additionally, the prevalence of online resources and courses makes it possible for many people to self-learn the more practical aspects of computer science (such as coding , video game development, and app design).

computer science , the study of computers and computing, including their theoretical and algorithmic foundations, hardware and software , and their uses for processing information. The discipline of computer science includes the study of algorithms and data structures, computer and network design, modeling data and information processes, and artificial intelligence . Computer science draws some of its foundations from mathematics and engineering and therefore incorporates techniques from areas such as queueing theory, probability and statistics , and electronic circuit design. Computer science also makes heavy use of hypothesis testing and experimentation during the conceptualization, design, measurement, and refinement of new algorithms, information structures, and computer architectures.

Computer science is considered as part of a family of five separate yet interrelated disciplines: computer engineering, computer science, information systems , information technology , and software engineering. This family has come to be known collectively as the discipline of computing. These five disciplines are interrelated in the sense that computing is their object of study, but they are separate since each has its own research perspective and curricular focus. (Since 1991 the Association for Computing Machinery [ACM], the IEEE Computer Society [IEEE-CS], and the Association for Information Systems [AIS] have collaborated to develop and update the taxonomy of these five interrelated disciplines and the guidelines that educational institutions worldwide use for their undergraduate, graduate, and research programs.)

The major subfields of computer science include the traditional study of computer architecture , programming languages , and software development. However, they also include computational science (the use of algorithmic techniques for modeling scientific data), graphics and visualization, human-computer interaction, databases and information systems, networks, and the social and professional issues that are unique to the practice of computer science. As may be evident, some of these subfields overlap in their activities with other modern fields, such as bioinformatics and computational chemistry . These overlaps are the consequence of a tendency among computer scientists to recognize and act upon their field’s many interdisciplinary connections.

Computer science emerged as an independent discipline in the early 1960s, although the electronic digital computer that is the object of its study was invented some two decades earlier. The roots of computer science lie primarily in the related fields of mathematics , electrical engineering, physics , and management information systems.

Mathematics is the source of two key concepts in the development of the computer—the idea that all information can be represented as sequences of zeros and ones and the abstract notion of a “ stored program .” In the binary number system , numbers are represented by a sequence of the binary digits 0 and 1 in the same way that numbers in the familiar decimal system are represented using the digits 0 through 9. The relative ease with which two states (e.g., high and low voltage) can be realized in electrical and electronic devices led naturally to the binary digit , or bit, becoming the basic unit of data storage and transmission in a computer system .

Electrical engineering provides the basics of circuit design—namely, the idea that electrical impulses input to a circuit can be combined using Boolean algebra to produce arbitrary outputs. (The Boolean algebra developed in the 19th century supplied a formalism for designing a circuit with binary input values of zeros and ones [false or true, respectively, in the terminology of logic] to yield any desired combination of zeros and ones as output.) The invention of the transistor and the miniaturization of circuits, along with the invention of electronic, magnetic, and optical media for the storage and transmission of information, resulted from advances in electrical engineering and physics.

Management information systems , originally called data processing systems, provided early ideas from which various computer science concepts such as sorting, searching, databases , information retrieval , and graphical user interfaces evolved. Large corporations housed computers that stored information that was central to the activities of running a business—payroll, accounting, inventory management, production control, shipping, and receiving.

Theoretical work on computability, which began in the 1930s, provided the needed extension of these advances to the design of whole machines; a milestone was the 1936 specification of the Turing machine (a theoretical computational model that carries out instructions represented as a series of zeros and ones) by the British mathematician Alan Turing and his proof of the model’s computational power. Another breakthrough was the concept of the stored-program computer, usually credited to Hungarian American mathematician John von Neumann . These are the origins of the computer science field that later became known as architecture and organization.

In the 1950s, most computer users worked either in scientific research labs or in large corporations. The former group used computers to help them make complex mathematical calculations (e.g., missile trajectories), while the latter group used computers to manage large amounts of corporate data (e.g., payrolls and inventories). Both groups quickly learned that writing programs in the machine language of zeros and ones was not practical or reliable. This discovery led to the development of assembly language in the early 1950s, which allows programmers to use symbols for instructions (e.g., ADD for addition) and variables (e.g., X ). Another program, known as an assembler , translated these symbolic programs into an equivalent binary program whose steps the computer could carry out, or “execute.”

Other system software elements known as linking loaders were developed to combine pieces of assembled code and load them into the computer’s memory, where they could be executed. The concept of linking separate pieces of code was important, since it allowed “libraries” of programs for carrying out common tasks to be reused. This was a first step in the development of the computer science field called software engineering.

Later in the 1950s, assembly language was found to be so cumbersome that the development of high-level languages (closer to natural languages) began to support easier, faster programming. FORTRAN emerged as the main high-level language for scientific programming, while COBOL became the main language for business programming. These languages carried with them the need for different software, called compilers , that translate high-level language programs into machine code. As programming languages became more powerful and abstract, building compilers that create high-quality machine code and that are efficient in terms of execution speed and storage consumption became a challenging computer science problem. The design and implementation of high-level languages is at the heart of the computer science field called programming languages.

Increasing use of computers in the early 1960s provided the impetus for the development of the first operating systems , which consisted of system-resident software that automatically handled input and output and the execution of programs called “jobs.” The demand for better computational techniques led to a resurgence of interest in numerical methods and their analysis, an activity that expanded so widely that it became known as computational science.

The 1970s and ’80s saw the emergence of powerful computer graphics devices, both for scientific modeling and other visual activities. (Computerized graphical devices were introduced in the early 1950s with the display of crude images on paper plots and cathode-ray tube [CRT] screens.) Expensive hardware and the limited availability of software kept the field from growing until the early 1980s, when the computer memory required for bitmap graphics (in which an image is made up of small rectangular pixels) became more affordable. Bitmap technology, together with high-resolution display screens and the development of graphics standards that make software less machine-dependent, has led to the explosive growth of the field. Support for all these activities evolved into the field of computer science known as graphics and visual computing.

Closely related to this field is the design and analysis of systems that interact directly with users who are carrying out various computational tasks. These systems came into wide use during the 1980s and ’90s, when line-edited interactions with users were replaced by graphical user interfaces (GUIs). GUI design, which was pioneered by Xerox and was later picked up by Apple (Macintosh) and finally by Microsoft ( Windows ), is important because it constitutes what people see and do when they interact with a computing device. The design of appropriate user interfaces for all types of users has evolved into the computer science field known as human-computer interaction (HCI).

The field of computer architecture and organization has also evolved dramatically since the first stored-program computers were developed in the 1950s. So called time-sharing systems emerged in the 1960s to allow several users to run programs at the same time from different terminals that were hard-wired to the computer. The 1970s saw the development of the first wide-area computer networks ( WANs ) and protocols for transferring information at high speeds between computers separated by large distances. As these activities evolved, they coalesced into the computer science field called networking and communications. A major accomplishment of this field was the development of the Internet .

The idea that instructions, as well as data, could be stored in a computer’s memory was critical to fundamental discoveries about the theoretical behaviour of algorithms . That is, questions such as, “What can/cannot be computed?” have been formally addressed using these abstract ideas. These discoveries were the origin of the computer science field known as algorithms and complexity. A key part of this field is the study and application of data structures that are appropriate to different applications. Data structures , along with the development of optimal algorithms for inserting, deleting, and locating data in such structures, are a major concern of computer scientists because they are so heavily used in computer software, most notably in compilers, operating systems, file systems, and search engines .

In the 1960s the invention of magnetic disk storage provided rapid access to data located at an arbitrary place on the disk. This invention led not only to more cleverly designed file systems but also to the development of database and information retrieval systems, which later became essential for storing, retrieving, and transmitting large amounts and wide varieties of data across the Internet. This field of computer science is known as information management.

Another long-term goal of computer science research is the creation of computing machines and robotic devices that can carry out tasks that are typically thought of as requiring human intelligence . Such tasks include moving, seeing, hearing, speaking, understanding natural language, thinking, and even exhibiting human emotions. The computer science field of intelligent systems, originally known as artificial intelligence (AI), actually predates the first electronic computers in the 1940s, although the term artificial intelligence was not coined until 1956.

Three developments in computing in the early part of the 21st century—mobile computing, client-server computing , and computer hacking—contributed to the emergence of three new fields in computer science: platform-based development, parallel and distributed computing , and security and information assurance . Platform-based development is the study of the special needs of mobile devices, their operating systems, and their applications. Parallel and distributed computing concerns the development of architectures and programming languages that support the development of algorithms whose components can run simultaneously and asynchronously (rather than sequentially), in order to make better use of time and space. Security and information assurance deals with the design of computing systems and software that protects the integrity and security of data, as well as the privacy of individuals who are characterized by that data.

Finally, a particular concern of computer science throughout its history is the unique societal impact that accompanies computer science research and technological advancements. With the emergence of the Internet in the 1980s, for example, software developers needed to address important issues related to information security, personal privacy, and system reliability. In addition, the question of whether computer software constitutes intellectual property and the related question “Who owns it?” gave rise to a whole new legal area of licensing and licensing standards that applied to software and related artifacts . These concerns and others form the basis of social and professional issues of computer science, and they appear in almost all the other fields identified above.

So, to summarize, the discipline of computer science has evolved into the following 15 distinct fields:

Computer science continues to have strong mathematical and engineering roots. Computer science bachelor’s, master’s, and doctoral degree programs are routinely offered by postsecondary academic institutions, and these programs require students to complete appropriate mathematics and engineering courses, depending on their area of focus. For example, all undergraduate computer science majors must study discrete mathematics (logic, combinatorics , and elementary graph theory ). Many programs also require students to complete courses in calculus , statistics , numerical analysis , physics, and principles of engineering early in their studies.

COMPSCI 389 : Research Methods in Computer Science

2022 semester two (1225) (15 points), course prescription, course overview.

This course is an overview of research methods and techniques used across Computer Science, including formal proof techniques and empirical methods that involve quantitative and/or qualitative data. Students will be expected to apply the research methods approach to develop a research proposal for a Computer Science research topic of their choice. Students will investigate a computing topic relevant to social or environmental responsibility in groups. The course finishes with a debate on a topic pertinent to the discipline.

Course Requirements

Capabilities developed in this course.

Learning Outcomes

• Describe the most common and well-established research methods employed in computer science research (Capability 1)
• Write an effective and feasible research proposal (Capability 1, 2, 3 and 5)
• Formulate an original and sound argument with scientific claims that are effectively supported by empirical evidence and/or formal constructions (proofs) (Capability 1, 2 and 5)
• Use electronic systems of bibliographic citation (Capability 1 and 5)
• Engage effectively in a peer-review review process of a piece of computer science research, both as a reviewer and reviewee (Capability 1 and 2)
• Engage productively in collaborative research that engages with a set of collaborators that is diverse culturally and/or in their areas of expertise on a topic relevant to social or environmental responsibility. (Capability 1, 3, 4, 5 and 6)
• Effectively communicate the results of a research project, by way of a poster and or presentation. (Capability 4 and 5)

Assessments

Special Requirements

Not applicable

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course you can expect 24 hours of contact hours, 82 hours assignments and test preparation, and 44 hours self-directed learning.

Delivery Mode

Campus experience.

Attendance is expected at scheduled activities including tutorials to complete components of the course. Lectures will be available as recordings. Other learning activities including seminars/tutorials will not be available as recordings. The course will include live online events including group discussions/tutorials. Attendance on campus is required for the test. The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html .

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html .

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

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• View all journals

Computer science articles from across Nature Portfolio

Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.

Holistic approach to carbon capture bridges the ‘Valley of Death’

Carbon-capture technology often founders at the point when basic research is translated into practical applications. A computational modelling platform called PrISMa solves this problem by considering the needs of all stakeholders.

A step forward in tracing and documenting dataset provenance

Training data are crucial for advancements in artificial intelligence, but many questions remain regarding the provenance of training datasets, license enforcement and creator consent. Mahari et al. provide a set of tools for tracing, documenting and sharing AI training data and highlight the importance for developers to engage with metadata of datasets.

• Nicholas Vincent

Switching between tasks can cause AI to lose the ability to learn

Artificial neural networks become incapable of mastering new skills when they learn them one after the other. Researchers have only scratched the surface of why this phenomenon occurs — and how it can be fixed.

• Razvan Pascanu

Latest Research and Reviews

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Novel concept-based image captioning models using LSTM and multi-encoder transformer architecture

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An improved sand cat swarm optimization with lens opposition-based learning and sparrow search algorithm

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News and Comment

Guide, don’t hide: reprogramming learning in the wake of AI

As artificial intelligence becomes increasingly integral to the world outside academia, universities face a crucial choice: to use or not to use.

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In the hills of eastern Tennessee, a record-breaking machine called Frontier is providing scientists with unprecedented opportunities to study everything from atoms to galaxies.

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Science treasures from Microsoft mogul up for auction — and researchers are salivating

Spacesuits, historic computers and more from the estate of the late Paul Allen are going on sale.

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AI made of jelly ‘learns’ to play Pong — and improves with practice

Inspired by neurons in a dish playing the classic video game, researchers show that synthetic hydrogels have a basic ‘memory’.

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Computer Science > General Literature

Title: research methods in computer science: the challenges and issues.

Abstract: Research methods are essential parts in conducting any research project. Although they have been theorized and summarized based on best practices, every field of science requires an adaptation of the overall approaches to perform research activities. In addition, any specific research needs a particular adjustment to the generalized approach and specializing them to suit the project in hand. However, unlike most well-established science disciplines, computing research is not supported by well-defined, globally accepted methods. This is because of its infancy and ambiguity in its definition, on one hand, and its extensive coverage and overlap with other fields, on the other hand. This article discusses the research methods in science and engineering in general and in computing in particular. It shows that despite several special parameters that make research in computing rather unique, it still follows the same steps that any other scientific research would do. The article also shows the particularities that researchers need to consider when they conduct research in this field.

Submission history

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References & Citations

• Google Scholar
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DBLP - CS Bibliography

Bibtex formatted citation.

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

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arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Undergraduate Research Opportunities

Research may be part of your coursework or as as part of individual research opportunities working with professors.

Learn about Harvard CS Faculty’s research by looking at the following Google spreadsheet on Faculty Research Interests and Office Hours . In addition to information about their research, it lists their office hours. Be sure to look at the info paragraph column to get a sense of what is the background needed to get involved with each particular research group.

Also considering taking a graduate course or advanced undergraduate course as a way to gain deeper knowledge in an area you are interested in. Many undergraduates take graduate courses, and many of these graduate courses involve reading research papers and engaging in a research project. This provides a great way to get involved in research within the context of a course, often in a small class setting.

We also recommend you check out the Computer Science colloquium to get a sense for what’s going on in the world of Computer Science Research.

Another way to get involved with research is to do a CS91r or senior thesis .

Other useful resources

Harvard College Office of Undergraduate Research and Fellowships Many opportunities for funding student research, including PRISE, Herchel Smith, and the Harvard College Research Program (HCRP).

SEAS-wide info on undergraduate research and FAQ

SEAS Undergrad Research Canvas Page (events and information)

Active Learning Labs

Student Employment Office: Research Opportunities

Harvard Innovation Labs

Remote Research Resources

How to get a research-based summer internship/job

REU Programs (Research Experience for Undergraduates funded by NSF):

• http://www.nsf.gov/crssprgm/reu/reu_search.jsp

Non-REU Programs:

• Lincoln Labs/MIT
• DAAD RISE (Germany)
• AT&T Research Internships
• DOE Science Undergraduate Laboratory Internships
• DOE Scholars Program
• Caltech Summer Undergraduate Research Fellowships
• Summer Undergraduate Research Fellowships, funded by NIST
• NCAR Computational Science
• National Security Agency
• Lawrence Livermore National Laboratory
• Privacy Tools for Sharing Research Data Project
• The Mind Project
• Radcliffe Research Partnernships

Harvard College offers a variety of research funding opportunities which are administered by the Office of Undergraduate Research and Fellowships . In particular, we’d like to point out PRISE via the Summer Residential Research Programs and the Harvard College Research Program (HCRP) via Independent Research Funding .

The Kempner Institute for the Study of Natural and Artificial Intelligence offers two undergraduate research programs for Harvard College undergraduates: a term-time program (KURE) and a 10-week summer program (KRANIUM). Please see their website for more information.

Though uncommon, sometimes faculty members may be able to pay for students to work during the semester. Please be aware, though, that Harvard does not allow students to receive academic credit for work for which they were compensated .

Harvard offers a Research Experience for Undergraduates (REU) Program for students to spend their summer performing research. Other universities also participate in REU programs for those who would like to do research elsewhere, as discussed above.

Travel Funding for Workshops, conferences, coding bootcamps, and other courses.

Always apply for grants from the hosting organization and check with your research advisor regarding any available funding for research-related presentations. Failing those options, the CS Area does have a small budget to support undergraduate student conference travel to present their research, please check with the DUS team.

The CS Diversity Committee allows students to apply for conference funding in support of women and underrepresented minorities in Computer Science.

The Office of Undergraduate Research and Fellowships offers funding for conferences . The URAF conference funding program supports Harvard College undergraduate students in presenting their original, independent research (poster or paper) at an academic conference. Awards are available year-round with a rolling deadline to apply for funding. Undergraduate students from all concentrations are encouraged to apply.

If your research also falls under Life and/or Physical Sciences and your lab is difficult to get to, then you might be eligible for transportation funding to get to your lab .

• Senior Thesis

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Research Experiences for Teachers in Engineering and Computer Science

View guidelines, important information about nsf’s implementation of the revised 2 cfr.

NSF Financial Assistance awards (grants and cooperative agreements) made on or after October 1, 2024, will be subject to the applicable set of award conditions, dated October 1, 2024, available on the NSF website . These terms and conditions are consistent with the revised guidance specified in the OMB Guidance for Federal Financial Assistance published in the Federal Register on April 22, 2024.

Important information for proposers

All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements. Submitting a proposal prior to a specified deadline does not negate this requirement.

Supports summer research experiences for K-14 educators that foster long-term collaborations between universities, community colleges, school districts and industry partners.

Sites and Supplements

The Research Experiences for Teachers (RET) in Engineering and Computer Science program supports authentic summer research experiences for K-14 educators to foster long-term collaborations between universities, community colleges, school districts, and industry partners. With this solicitation, the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE) focus on a reciprocal exchange of expertise between K-14 educators and research faculty and (when applicable) industry mentors. K-14 educators will enhance their scientific disciplinary knowledge in engineering or computer science and translate their research experiences into classroom activities and curricula to broaden their students’ awareness of and participation in computing and engineering pathways. At the same time, the hosting research faculty will deepen their understanding of classroom practices, current curricula, pedagogy, and K-14 educational environments.

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• November 8, 2021 - Research Experiences for Teachers (RET) in Engineering and…
• October 12, 2021 - Research Experiences for Teachers (RET) in Engineering and…

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• Directorate for Engineering (ENG)
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Sept 2025 Entry

The Department of Computing has established a solid foundation in Algorithm and Theory, Machine Learning and Artificial Intelligence, Big Data Analytics, Cyber Security and Privacy, Networking and Mobile Computing, and System Modelling and Software Engineering.

Our students’ cutting-edge research is supported by enthusiastic faculty members and support staff who ensure that the Department provides them with a positive and nurturing academic environment.

The Department also ensures that all research students have opportunities to enrich their research experience through research exchange visits, attendance at leading international conferences and participation in global competitions and departmental projects.

The Department’s global network crosses disciplines and bridges industries, offering our students ample career opportunities, resources and access to research funding.

Artificial Intelligence and Big Data Computing

Our researchers of Artificial Intelligence and Big Data Computing bring together the efforts from diverse areas to deliver high impact research. Among others, their investigations cover parallel databases as services; data accountability and service outsourcing; data and communicative behaviour in online social networks; effective search engine indexing; transfer-learning methods for multisource data sets; and social media big data analysis. Our group also works on various kinds of machine learning models, such as deep learning and transfer learning models, while applying these in the design of robots. We also work on social robotics, affective computing, and artificial creativity as well.

Please click here to find out more about the research interests of our Department of Computing and contact our staff directly to discuss research opportunities.

Blockchain, Cyber Security and Privacy

Cyber Security and Privacy address a wide range of security and privacy issues that have a profound impact on securing the cyberspace. Researchers in the group have expertise in areas such as the financial technology, blockchain, post-quantum computing security, mobile applications, Internet of Things (IoT), and the underlying Internet infrastructure. They have a close relationship with industries and have successfully transferred our knowledge to solve security and privacy problems in the real world.

Graphics, Multimedia and Virtual Reality

Multimedia combines content such as sound, images and graphics to make applications dynamic in areas such as education, entertainment, social networking and telemedicine. The key research challenges lie in managing complex multimedia objects and extending them into three dimensions with the capacity for real-time interaction. Our researchers focus on three-dimensional computer graphic modelling and rendering, distributed three-dimensional graphics, image and video quality enhancement, content-based image retrieval, and multi-sensor data and motion analysis.

Networking and Mobile Computing

Networking and Mobile Computing is concerned with designing efficient communications protocols and architectures for exchanging data among computers and mobile devices, enabling a wide range of networked applications with enhanced real-world experience of network and mobile users. Research areas investigated by the group faculty include but are not limited to edge computing, smart sensing and networking, pervasive and mobile computing, Internet of Things, and network measurement.

Pattern Recognition and Natural Language Processing

Pattern recognition, or the classification of measurements and observations, is significantly enhanced through the application of computational intelligence techniques. With surging demand for efficient and high-performance automated pattern recognition, breakthroughs are being made in emerging areas such as video and image processing, medical imaging, biometric security, Web intelligence, social media mining, and human-machine interaction with language, among others,  with applications to smart city development and health care.

Systems Modelling and Software Engineering

Systems and software engineering involves the development of methodologies, processes and tools for building robust, high performance computer-based systems. In this area, our researchers have applied their expertise on key challenges in agile development, context-aware middleware, cost estimation, cyber-physical systems, real-time embedded systems, software metrics, software processes and quality, storage in embedded systems and risk management. They are also working on solutions to the problems arising from the use of business-critical applications.

Departmental Research Centre and Laboratories

Currently, the Department has the University Research Facility in Big Data Analytics (UBDA), Biometrics Research and Innovation Centre, Data Science and AI Lab (DaSAIL), FinTech and Cyber Security Lab (FCSL), Internet and Mobile Computing Lab (IMCL), PolyU-Yonyou Joint Laboratory on Smart Cloud Computing and Research Centre on Data Science and Artificial Intelligence (RC-DSAI).

Private Cloud and GPU clusters

A private cloud has been built to provide a fully integrated, self-service, scalable virtualised platform. The platform is widely and intensely used by our staff and students, greatly facilitating their research progress. With the strong need for deep learning related research, the Department is establishing GPU clusters.

An electroencephalogram (EEG) system was purchased to support research on cognitive computing and related areas for applications on smart healthcare, smart living and smart education. The system promotes high-impact interdisciplinary research on fundamental scientific problems, such as investigating the neural systems and internal activities of the brain, the analysis and prediction of human behaviour and emotion and the diagnosis and rehabilitation of mental disease.

Virtual and Augmented Reality System

The Department has purchased equipment for virtual reality (VR) and augmented reality (AU) research, including (1) a projection VR system that delivers high-end visuals with a mixed reality (VR + AR) environment, in which multiple users can share their data, design and visual experiences; (2) a walking VR system that visualises, navigates and interacts within 25´25 sq. metres; the user can freely move, touch and interact in a large tracking space; (3) a standing VR system that can be used with home-use VR systems; and (4) a mobile VR system that can be used on a smartphone.

Compulsory - Two Academic Referee’s Reports are required for the application for Fellowship and Scholarship Schemes.

• Identify and invite two academics who are familiar with your academic performance and achievements. Proposed supervisor(s) from the PolyU, proposed supervisor(s) from the partner institution (if any), and persons from non-academic backgrounds are not considered appropriate academic referees. 
• Provide complete and accurate information on your referees, including their email addresses under a university or an organisation, in your online application.

Compulsory - A standard form must be used for the submission of research proposal.  Please click here  to download the form.

Compulsory – Please upload all academic qualifications including Bachelor’s degree and Master’s degree (if any) according to the University’s admission requirements , also refer to the ‘ Procedures – Guidelines for Submitting Supporting Documents ’ to follow the submission requirements.

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Recommendations for Using 3D Printing to Implement Integrated STEM Design Projects in Queensland Schools: A Mixed-methods Survey Study

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Georgia’s stem high schools alumni perceived preparedness for college stem programs: a qualitative case study, set up and delivery of electrical and computer engineering projects at undergraduate engineering universities for outreach and partnership with k-12 stem schools.

This paper discusses the set up and delivery of Electrical and Computer Engineering (ECE) projects with critical Science, Technology, Engineering, and Mathematics (STEM) components as part of "Engage K-12 students in ECE", the outreach and partnership ...

STEM Outreach with K-12 Schools: Delivery of Electrical Engineering Projects and Assessment of Learning Outcomes

This Research to Practice Work-in-Progress paper discusses the delivery and assessment of the learning outcomes for hands-on STEM laboratory and project activities which engage K-12 STEM students and K-12 STEM educators. Hands-on laboratory and project-...

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New York, NY, United States

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