phd in computer science topics

Research Topics & Ideas: CompSci & IT

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

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

NB – This is just the start…

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

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

Overview: CompSci Research Topics

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

Topics/Ideas: Algorithms & Data Structures

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

Topics & Ideas: Artificial Intelligence (AI)

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

Topics & Ideas: Networking

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

Topics & Ideas: Database Systems

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

Topics & Ideas: Human-Computer Interaction

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

Topics & Ideas: Information Security

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

Topics & Ideas: Software Engineering

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

CompSci & IT Dissertations/Theses

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

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

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

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

Fast-Track Your Research Topic

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

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PhD in Computer Science

The PhD in Computer Science is a small and selective program at Pace University that aims to cultivate advanced computing research scholars and professionals who will excel in both industry and academia. By enrolling in this program, you will be on your way to joining a select group at the very nexus of technological thought and application.

Learn more about the PhD in Computer Science .

Forms and Research Areas

General forms.

• PhD Policies and Procedures Manual – The manual contains all the information you need before, during, and toward the end of your studies in the PhD program.
• Advisor Approval Form (PDF) – Completed by student and approved by faculty member agreeing to the role as advisor.
• Committee Member Approval Form (PDF) – Completed by student with signatures of each faculty member agreeing to be on dissertation committee.
• Change in Advisor or Committee Member Approval Form (PDF) – Completed by student with the approval of new advisor or committee member. Department Chair approval needed.
• Qualifying Exam Approval Form (PDF) – Complete and return form to the Program Coordinator no later than Week 6 of the semester.

Dissertation Proposal of Defense Forms

• Application for the Dissertation Proposal of Defense Form (PDF) – Completed by student with the approval of committee members that dissertation proposal is sufficient to defend. Completed form and abstract and submitted to program coordinator for scheduling of defense.
• Dissertation Proposal Defense Evaluation Form (PDF) – To be completed by committee members after student has defended his dissertation proposal.

Final Dissertation Defense Forms

• Dissertation Pre- Defense Approval Form (PDF) – Committee approval certifying that the dissertation is sufficiently developed for a defense.
• Dissertation Defense Evaluation Form (PDF) – Completed by committee members after student has defended his dissertation.

All completed forms submitted to the program coordinator.

Research Areas

The Seidenberg School’s PhD in Computer Science covers a wealth of research areas. We pride ourselves on engaging with every opportunity the computer science field presents. Check out some of our specialties below for examples of just some of the topics we cover at Seidenberg. If you have a particular field of study you are interested in that is not listed below, just get in touch with us and we can discuss opportunities and prospects.

Some of the research areas you can explore at Seidenberg include:

Algorithms And Distributed Computing

Algorithms research in Distributed Computing contributes to a myriad of applications, such as Cloud Computing, Grid Computing, Distributed Databases, Cellular Networks, Wireless Networks, Wearable Monitoring Systems, and many others. Being traditionally a topic of theoretical interest, with the advent of new technologies and the accumulation of massive volumes of data to analyze, theoretical and experimental research on efficient algorithms has become of paramount importance. Accordingly, many forefront technology companies base 80-90% of their software-developer hiring processes on foundational algorithms questions. The Seidenberg faculty has internationally recognized strength in algorithms research for Ad-hoc Wireless Networks embedded in IoT Systems, Mobile Networks, Sensor Networks, Crowd Computing, Cloud Computing, and other related areas. Collaborations on these topics include prestigious research institutions world-wide.

Machine Learning In Medical Image Analysis

Machine learning in medical imaging is a potentially disruptive technology. Deep learning, especially convolutional neural networks (CNN), have been successfully applied in many aspects of medical image analysis, including disease severity classification, region of interest detection, segmentation, registration, disease progression prediction, and other tasks. The Seidenberg School maintains a research track on applying cutting-edge machine learning methods to assist medical image analysis and clinical data fusion. The purpose is to develop computer-aided and decision-supporting systems for medical research and applications.

Pattern recognition, artificial intelligence, data mining, intelligent agents, computer vision, and data mining are topics that are all incorporated into the field of robotics. The Seidenberg School has a robust robotics program that combines these topics in a meaningful program which provides students with a solid foundation in the robotics sphere and allows for specialization into deeper research areas.

Cybersecurity

The Seidenberg School has an excellent track record when it comes to cybersecurity research. We lead the nation in web security, developing secure web applications, and research into cloud security and trust. Since 2004, Seidenberg has been designated a Center of Academic Excellence in Information Assurance Education three times by the National Security Agency and the Department of Homeland Security and is now a Center of Academic Excellence in Cyber Defense Education. We also secured more than $2,000,000 in federal and private funding for cybersecurity research during the past few years.

Pattern Recognition And Machine Learning

Just as humans take actions based on their sensory input, pattern recognition and machine learning systems operate on raw data and take actions based on the categories of the patterns. These systems can be developed from labeled training data (supervised learning) or from unlabeled training data (unsupervised learning). Pattern recognition and machine learning technology is used in diverse application areas such as optical character recognition, speech recognition, and biometrics. The Seidenberg faculty has recognized strengths in many areas of pattern recognition and machine learning, particularly handwriting recognition and pen computing, speech and medical applications, and applications that combine human and machine capabilities.

A popular application of pattern recognition and machine learning in recent years has been in the area of biometrics. Biometrics is the science and technology of measuring and statistically analyzing human physiological and behavioral characteristics. The physiological characteristics include face recognition, DNA, fingerprint, and iris recognition, while the behavioral characteristics include typing dynamics, gait, and voice. The Seidenberg faculty has nationally recognized strength in biometrics, particularly behavioral biometrics dealing with humans interacting with computers and smartphones.

Big Data Analytics

The term “Big Data” is used for data so large and complex that it becomes difficult to process using traditional structured data processing technology. Big data analytics is the science that enables organizations to analyze a mixture of structured, semi-structured, and unstructured data in search of valuable information and insights. The data come from many areas, including meteorology, genomics, environmental research, and the internet. This science uses many machine learning algorithms and the challenges include data capture, search, storage, analysis, and visualization.

Business Process Modeling

Business Process Modeling is the emerging technology for automating the execution and integration of business processes. The BPMN-based business process modeling enables precise modeling and optimization of business processes, and BPEL-based automatic business execution enables effective computing service and business integration and effective auditing. Seidenberg was among the first in the nation to introduce BPM into curricula and research.

Educational Approaches Using Emerging Computing Technologies

The traditional classroom setting doesn’t suit everyone, which is why many teachers and students are choosing to use the web to teach, study, and learn. Pace University offers online bachelor's degrees through NACTEL and Pace Online, and many classes at the Seidenberg School and Pace University as a whole are available to students online.

The Seidenberg School’s research into new educational approaches include innovative spiral education models, portable Seidenberg labs based on cloud computing and computing virtualization with which students can work in personal enterprise IT environment anytime anywhere, and creating new semantic tools for personalized cyber-learning.

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Computer Science, PhD

Researchers from all fields use computational models to analyze massive amounts of data. There’s a growing need for computer scientists who can collaborate with other domains and also research ways to improve the networks, the operating systems, and the multitude of devices that are integrated into our daily lives.

Our PhD in Computer Science program prepares you for a career in research and/or teaching by providing the necessary course work and collaborative environment for both supervised and independent research. Our PhD students are researching mobile apps to help improve the science of learning, building operating systems for high-performance computers, addressing security and privacy from a data-oriented perspective, improving computer performance, and more.

You’ll have the opportunity to take part in the diverse faculty research collaborations with other departments and programs within the university, such as the Learning Research and Development Center, the School of Engineering, and the School of Medicine.

Degree Requirements

Course requirements.

The PhD degree requires 72 credits of formal course work, independent study, directed study, and/or dissertation research. In addition to the credit requirement, twelve courses are required for the PhD categorized as follows: four foundation courses, six elective courses,  CS 2001  (Research Topics in Computer Science) and  CS 2002  (Research Experiences in Computer Science). CS 2001 must be taken during the first fall term and CS 2002 must be taken during the following spring term.

The four foundation courses must cover each of the following four foundation areas.

Architecture and Compilers

• CS 2410 - Computer Architecture OR
• CS 2210 - Compiler Design

Operating Systems and Networks

• ​ CS 2510 - Computer Operating Systems OR
• CS 2520 - Wide Area Networks

Artifical Intelligence and Database Systems

• CS 2710 - Foundations of Artificial Intelligence OR
• CS 2550 - Principles of Database Systems

Theory and Algorithms

• CS 2110 - Introduction to Theory of Computation OR
• CS 2150 - Design and Analysis of Algorithms

The six elective courses must be 2100-level or higher CSD courses and cannot be independent study courses ( CS 2990 ,  CS 3000 ), graduate internship ( CS 2900 ), thesis project or research courses ( CS 2910 ,  CS 3900 ). At least two of the six courses must be at the 3000-level.

The following requirements apply to the 12 required courses:

• All must be taken for a letter grade.
• Students are required to complete the four required foundation area courses by the end of the fourth regular term of study. Regular terms include the fall and spring and do not include the summer session.
• The student must receive a grade of B or better in each of the required foundation area courses, and a grade of B-or better in each of the six additional courses; in addition, he or she must maintain an overall average QPA of 3.0 or better.
• No more than 6 of the 12 courses may be taken outside of the CSD. This includes up to four courses that are transfered from other universities at the time of admission. All courses from outside the CSD must be approved by GPEC.
• All 12 courses must be successfully completed before admission to candidacy for the PhD (This normally occurs when the student passes the oral examination during the dissertation proposal.)

For full degree requirements details, visit the Computer Science course catalog .

Admissions Requirements

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Academics   /   Graduate PhD in Computer Science

The doctor of philosophy in computer science program at Northwestern University primarily prepares students to become expert independent researchers. PhD students conduct original transformational research in extant and emerging computer science topics. Students work alongside top researchers to advance the core CS fields from Theory to AI and Systems and Networking . In addition, PhD students have the opportunity to collaborate with CS+X faculty who are jointly appointed between CS and disciplines including business, law, economics, journalism, and medicine.

Joining a Track

Doctor of philosophy in computer science students follow the course requirements, qualifying exam structure, and thesis process specific to one of five tracks :

• Artificial Intelligence and Machine Learning
• Computer Engineering

Within each track, students explore many areas of interest, including programming languages , security and privacy and human-computer interaction .

Learn more about computer science research areas

Curriculum and Requirements

The focus of the CS PhD program is learning how to do research by doing research, and students are expected to spend at least 50% of their time on research. Students complete ten graduate curriculum requirements (including COMP_SCI 496: Introduction to Graduate Studies in Computer Science ), and additional course selection is tailored based on individual experience, research track, and interests. Students must also successfully complete a qualifying exam to be admitted to candidacy.

CS PhD Manual Apply now

Request More Information

Download a PDF program guide about your program of interest and get in contact with our graduate admissions staff.

Request info about the PhD degree

Opportunities for PhD Students

Cognitive science certificate.

Computer science PhD students may earn a specialization in cognitive science by taking six cognitive science courses. In addition to broadening a student’s area of study and improving their resume, students attend cognitive science events and lectures, they can receive conference travel support, and they are exposed to cross-disciplinary exchanges.

The Crown Family Graduate Internship Program

PhD candidates may elect to participate in the Crown Family Graduate Internship Program. This opportunity allows the doctoral candidate to gain practical experience in industry or in national research laboratories in areas closely related to their research.

Management for Scientists and Engineers Certificate Program

The certificate program — jointly offered by The Graduate School and Kellogg School of Management — provides post-candidacy doctoral students with a basic understanding of strategy, finance, risk and uncertainty, marketing, accounting and leadership. Students are introduced to business concepts and specific frameworks for effective management relevant to both for-profit and nonprofit sectors.

Career Paths

Recent graduates of the computer science PhD program are pursuing careers in industry & research labs, academia, and startups.

• Georgia Institute of Technology
• Illinois Institute of Technology
• Northeastern
• University of Pittsburgh
• University of Rochester
• University of Washington
• Naval Research Laboratory
• Northwestern University

Industry & Research Labs

• Adobe Research
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What Students Are Saying

"One great benefit of Northwestern is the collaborative effort of the CS department that enabled me to work on projects involving multiple faculty, each with their own diverse set of expertise.

Northwestern maintains a great balance: you will work on leading research at a top-tier institution, and you won't get lost in the mix."

— Brian Suchy, PhD Candidate, Computer Systems

What Alumni Are Saying

"In the early stage of my PhD program, I took several courses from the Department of Economics and the Kellogg School of Management and, later, I started collaborating with researchers in those areas. The experience taught me how to have an open mind to embrace and work with people with different backgrounds."

— Yiding Feng (PhD '21), postdoctoral researcher, Microsoft Research Lab – New England

Read an alumni profile of Yiding Feng

"My work at IBM Research involves bringing together symbolic and deep learning techniques to solve problems in interpretable, effective ways, which means I must draw upon the research I did at Northwestern quite frequently."

— Maxwell Crouse (PhD '21), AI Research Scientist, IBM Research

Read an alumni profile of Maxwell Crouse

The theory group here is very warm and close-knit. Starting a PhD is daunting, and it is comforting to have a community I can lean on.

— Vaidehi Srinivas, PhD Candidate, CS Theory

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Computer Science Ph.D. Program

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The Cornell Ph.D. program in computer science is consistently ranked among the top six departments in the country, with world-class research covering all of computer science. Our computer science program is distinguished by the excellence of the faculty, by a long tradition of pioneering research, and by the breadth of its Ph.D. program. Faculty and Ph.D. students are located both in Ithaca and in New York City at the Cornell Tech campus . The Field of Computer Science also includes faculty members from other departments (Electrical Engineering, Information Science, Applied Math, Mathematics, Operations Research and Industrial Engineering, Mechanical and Aerospace Engineering, Computational Biology, and Architecture) who can supervise a student's Ph.D. thesis research in computer science.

Over the past years we've increased our strength in areas such as artificial intelligence, computer graphics, systems, security, machine learning, and digital libraries, while maintaining our depth in traditional areas such as theory, programming languages and scientific computing.  You can find out more about our research here . 

The department provides an exceptionally open and friendly atmosphere that encourages the sharing of ideas across all areas. 

Cornell is located in the heart of the Finger Lakes region. This beautiful area provides many opportunities for recreational activities such as sailing, windsurfing, canoeing, kayaking, both downhill and cross-country skiing, ice skating, rock climbing, hiking, camping, and brewery/cider/wine-tasting. In fact, Cornell offers courses in all of these activities.

The Cornell Tech campus in New York City is located on Roosevelt Island.  Cornell Tech  is a graduate school conceived and implemented expressly to integrate the study of technology with business, law, and design. There are now over a half-dozen masters programs on offer as well as doctoral studies.

FAQ with more information about the two campuses .

Ph.D. Program Structure

Each year, about 30-40 new Ph.D. students join the department. During the first two semesters, students become familiar with the faculty members and their areas of research by taking graduate courses, attending research seminars, and participating in research projects. By the end of the first year, each student selects a specific area and forms a committee based on the student's research interests. This “Special Committee” of three or more faculty members will guide the student through to a Ph.D. dissertation. Ph.D. students that decide to work with a faculty member based at Cornell Tech typically move to New York City after a year in Ithaca.

The Field believes that certain areas are so fundamental to Computer Science that all students should be competent in them. Ph.D. candidates are expected to demonstrate competency in four areas of computer science at the high undergraduate level: theory, programming languages, systems, and artificial intelligence.

Each student then focuses on a specific topic of research and begins a preliminary investigation of that topic. The initial results are presented during a comprehensive oral evaluation, which is administered by the members of the student's Special Committee. The objective of this examination, usually taken in the third year, is to evaluate a student's ability to undertake original research at the Ph.D. level.

The final oral examination, a public defense of the dissertation, is taken before the Special Committee.

To encourage students to explore areas other than Computer Science, the department requires that students complete an outside minor. Cornell offers almost 90 fields from which a minor can be chosen. Some students elect to minor in related fields such as Applied Mathematics, Information Science, Electrical Engineering, or Operations Research. Others use this opportunity to pursue interests as diverse as Music, Theater, Psychology, Women's Studies, Philosophy, and Finance.

The computer science Ph.D. program complies with the requirements of the Cornell Graduate School , which include requirements on residency, minimum grades, examinations, and dissertation.

The Department also administers a very small 2-year Master of Science program (with thesis). Students in this program serve as teaching assistants and receive full tuition plus a stipend for their services.

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Phd program, find your passion for research.

Duke Computer Science gives incoming students an opportunity to investigate a range of topics, research problems, and research groups before committing to an advisor in the first year. Funding from the department and Duke makes it possible to attend group meetings, seminars, classes and colloquia. Students may work on multiple problems simultaneously while finding the topic that will motivate them through their first project. Sharing this time of learning and investigation with others in the cohort helps create lasting collaborators and friends.

Write a research proposal the first year and finish the research the second under the supervision of the chosen advisor and committee; present the research results to the committee and peers. Many students turn their RIP work into a conference paper and travel to present it.

Course work requirements are written to support the department's research philosophy. Pass up to four of the required six courses in the first two years to give time and space for immersing oneself in the chosen area.

Years three through five continue as the students go deeper and deeper into a research area and their intellectual community broadens to include collaborators from around the world. Starting in year three, the advisor funds the student's work, usually through research grants. The Preliminary exam that year is the opportunity for the student to present their research to date, to share work done by others on the topic, and to get feedback and direction for the Ph.D. from the committee, other faculty, and peers.

Most Ph.D students defend in years five and six. While Duke and the department guarantee funding through the fifth year, advisors and the department work with students to continue support for work that takes longer.

Teaching is a vital part of the Ph.D. experience. Students are required to TA for two semesters, although faculty are ready to work with students who want more involvement. The Graduate School's Certificate in College Teaching offers coursework, peer review, and evaluation of a teaching portfolio for those who want to teach. In addition, the Department awards a Certificates of Distinction in Teaching for graduating PhD students who have demonstrated excellence in and commitment to teaching and mentoring.

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PhD Program

We are proud of the quality of PhD students we attract and the training they receive. All of our students receive support, including an annual stipend, in the form of external and internal competitive fellowships, research fellowships, or teaching fellowships. As a PhD candidate, you will share in the excitement of discovery as you collaborate with our faculty on cutting-edge research . You will also acquire strong independent research skills and begin to develop your own reputation as a member of the research community.

Because the advisor-graduate relationship is the cornerstone of a successful PhD experience, all new PhD candidates are carefully matched with faculty advisors based on mutual research interests. In addition, an active three-person PhD committee is created for each PhD student to provide cogent advice throughout your degree program.

You will find the work here challenging and personally rewarding. Students who complete our PhD program are well-prepared for careers in academia, research, government, and industry. Please visit the Graduate Admissions information page  for application requirements, deadlines, and other important information.

Application Deadlines:

• The PhD deadline for fall is December 15th. (No recruiting for spring admissions.)
• The application will be available for submission on or around August 15.

To learn more about the PhD admissions process, please visit our PhD Admissions FAQ page .

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Graduate Program

In the Computer Science program, you will learn both the fundamentals of computation and computation’s interaction with the world. Your work will involve a wide range of areas including theoretical computer science, artificial intelligence and machine learning, economics and computer science, privacy and security, data-management systems, intelligent interfaces, operating systems, computer graphics, computational linguistics, robotics, networks, architectures, program languages, and visualization.

You will be involved with researchers in several interdisciplinary initiatives across the University, such as the Center for Research on Computation and Society , the Data Science Initiative , and the Berkman Klein Center for Internet and Society .

Examples of projects current and past students have worked on include leveraging machine learning to solve real-world sequential decision-making problems and using artificial intelligence to help conservation and anti-poaching efforts around the world.

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Computer Science Degree

Harvard School of Engineering offers a  Doctor of Philosophy (Ph.D) degree in Computer Science , conferred through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences. Prospective students apply through Harvard Griffin GSAS; in the online application, select “Engineering and Applied Sciences” as your program choice and select "PhD Computer Science" in the Area of Study menu.

In addition to the Ph.D. in Computer Science, the Harvard School of Engineering also offers master’s degrees in  Computational Science and Engineering as well as in Data Science which may be of interest to applicants who wish to apply directly to a master’s program.

Computer Science Career Paths

Graduates of the program have gone on to a range of careers in industry in companies like Riot Games as game director and Lead Scientist at Raytheon. Others have positions in academia at University of Pittsburgh, Columbia, and Stony Brook.

Admissions & Academic Requirements

Prospective students apply through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS). In the online application, select  “Engineering and Applied Sciences” as your program choice and select "PhD Engineering Sciences: Electrical Engineering​." Please review the  admissions requirements and other information  before applying. Our website also provides  admissions guidance ,  program-specific requirements , and a  PhD program academic timeline . In the application for admission, select “Engineering and Applied Sciences” as your degree program choice and your degree and area of interest from the “Area of Study“ drop-down. PhD applicants must complete the Supplemental SEAS Application Form as part of the online application process.

Academic Background

Applicants typically have bachelor’s degrees in the natural sciences, mathematics, computer science, or engineering.

Standardized Tests

GRE General: Not Accepted

Computer Science Faculty & Research Areas

View a list of our computer science faculty  and  computer science affiliated research areas . Please note that faculty members listed as “Affiliates" or "Lecturers" cannot serve as the primary research advisor.

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Graduate student clubs and organizations bring students together to share topics of mutual interest. These clubs often serve as an important adjunct to course work by sponsoring social events and lectures. Graduate student clubs are supported by the Harvard Kenneth C. Griffin School of Arts and Sciences. Explore the list of active clubs and organizations .

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Learn more about financial support for PhD students.

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PhD in Computer Science Topics 2023: Top Research Ideas

Starlink: A Game Changer for Working from Home

If you want to embark on a  PhD  in  computer science , selecting the right  research topics  is crucial for your success. Choosing the appropriate  thesis topics  and research fields will determine the direction of your research. When selecting thesis topics for your research project, it is crucial to consider the compelling and relevant issues. The topic selection can greatly impact the success of your project in this field.

We’ll delve into various areas and subfields within  computer science research , exploring different projects, technologies, and ideas to help you narrow your options and find the perfect thesis topic. Whether you’re interested in  computer science research topics  like  artificial intelligence ,  data mining ,  cybersecurity , or any other  cutting-edge field  in computer science engineering, we’ve covered you with various research fields and analytics.

Stay tuned as we discuss how a well-chosen topic can shape your research proposal, journal paper writing process, thesis writing journey, and even individual chapters. We will address the topic selection issues and analyze how it can impact your communication with scholars. We’ll provide tips and insights to help research scholars and experts select high-quality topics that align with their interests and contribute to the advancement of knowledge in technology. These tips will be useful when submitting articles to a journal in the field of computer science.

Top PhD research topics in computer science for 2024

Exploration of Cutting-Edge Research Areas

As a Ph.D. student in computer science, you can delve into cutting-edge research areas such as technology, cybersecurity, and applications. These fields are shaping the future of deep learning and the overall evolution of computer science. One such computer science research field is  quantum computing , which explores the principles of quantum mechanics to develop powerful computational systems. It is an area that offers various computer science research topics and has applications in cybersecurity. By studying topics like quantum  algorithms  and quantum information theory, you can contribute to advancements in this exciting field. These advancements can be applied in various applications, including deep learning techniques. Moreover, your research in this area can also contribute to your thesis.

Another burgeoning research area is  artificial intelligence (AI) . With the rise of deep learning and the increasing integration of AI into various applications, there is a growing need for researchers who can push the boundaries of AI technology in cybersecurity and big data. As a PhD student specializing in AI, you can explore deep learning, natural language processing, and computer vision and conduct research in the field. These techniques have various applications and require thorough analysis. Your research could lead to breakthroughs in autonomous vehicles, healthcare diagnostics, robotics, applications, deep learning, cybersecurity, and the internet.

Discussion on Emerging Fields

In addition to established research areas, it’s important to consider emerging fields, such as deep learning, that hold great potential for innovation in applications and techniques for cybersecurity. One such field is cybersecurity. With the increasing number of cyber threats and attacks, experts in the cybersecurity field are needed to develop robust security measures for the privacy and protection of internet users. As a PhD researcher in cybersecurity, you can investigate topics like network security, cryptography, secure software development, applications, internet privacy, and thesis. Your work in the computer science research field could contribute to safeguarding sensitive data and protecting critical infrastructure by enhancing security and privacy in various applications.

Data mining is an exciting domain that offers ample opportunities for research in deep learning techniques and their analysis applications. With the rise of cloud computing, extracting valuable insights from vast amounts of data has become crucial across industries. Applications, research topics, and techniques in cloud computing are now essential for uncovering valuable insights from the data generated daily. By focusing your PhD studies on data mining techniques and algorithms, you can help organizations make informed decisions based on patterns and trends hidden within large datasets. This can have significant applications in privacy management and learning.

Bioinformatics is an emerging field that combines computer science with biology and genetics, with applications in big data, cloud computing, and thesis research. As a Ph.D. student in bioinformatics, you can leverage computational techniques and applications to analyze biological data sets and gain insights into complex biological processes. The thesis could focus on the use of cloud computing for these analyses. Your research paper could contribute to advancements in personalized medicine or genetic engineering applications. Your thesis could focus on learning and the potential applications of your findings.

Highlighting Interdisciplinary Topics

Computer science intersects with cloud computing, fog computing, big data, and various other disciplines, opening up avenues for interdisciplinary research. One such area is healthcare informatics, where computer scientists work alongside medical professionals to develop innovative solutions for healthcare challenges using cloud computing and fog computing. The collaboration involves the management of these technologies to enhance healthcare outcomes. As a PhD researcher in healthcare informatics, you can explore electronic health records, medical imaging analysis, telemedicine, security, learning, management, and cloud computing. Your work in healthcare management could profoundly impact improving patient care and streamlining healthcare systems, especially with the growing importance of learning and implementing IoT technology while ensuring security.

Computational social sciences is an interdisciplinary field that combines computer science with social science methodologies, including cloud computing, fog computing, edge computing, and learning. Studying topics like social networks or sentiment analysis can give you insights into human behavior and societal dynamics. This learning can be applied to mobile ad hoc networks (MANETs) security management. Your research on learning, security, cloud computing, and IoT could contribute to understanding and addressing complex social issues such as online misinformation or spreading infectious diseases through social networks.

Guidance on selecting thesis topics for computer science PhD scholars

Importance of aligning personal interests with current trends and gaps in existing knowledge.

Choosing a thesis topic is an important decision for  computer science PhD scholars , especially in IoT. It is essential to consider topics related to learning, security, and management to ensure a well-rounded research project. It is essential to align personal interests with current trends in learning, management, security, and IoT and fill gaps in existing knowledge. By choosing a learning topic that sparks your passion for management, you are more likely to stay motivated throughout the research process on the cutting edge of IoT. Aligning your interests with the latest advancements in cloud computing and fog computing ensures that your work in computer science contributes to the field’s growth. Additionally, staying updated on the latest developments in learning and management is essential for your professional development.

Conducting thorough literature reviews is vital to identify potential research gaps in the field of learning management and security. Additionally, it is important to consider the edge cases and scenarios that may arise. Dive into relevant academic journals, conferences, and publications to understand current research in learning management, security, and mobile. Look for areas with limited studies or conflicting findings in security, fog, learning, and management, indicating potential gaps that need further exploration. By identifying these learning and management gaps, you can contribute new insights and expand the existing knowledge on security and fog.

Tips on Conducting Thorough Literature Reviews to Identify Potential Research Gaps

When conducting literature reviews on mobile learning management, it is important to be systematic and comprehensive while considering security. Here are some tips for effective mobile security management and learning. These tips will help you navigate this process effectively.

• Start by defining specific keywords related to your research area, such as security, learning, mobile, and edge, and use them when searching for relevant articles.
• Utilize academic databases like IEEE Xplore, ACM Digital Library, and Google Scholar for comprehensive cloud computing, edge computing, security, and machine learning coverage.
• Read abstracts and introductions of articles on learning, security, blockchain, and cloud computing to determine their relevance before diving deeper into full papers.
• Take notes while learning about security in cloud computing to keep track of key findings, methodologies used, and potential research gaps.
• Look for recurring themes or patterns in different studies related to learning, security, and cloud computing that could indicate areas needing further investigation.

By following these steps, you can clearly understand the existing literature landscape in the fields of learning, security, and cloud computing and identify potential research gaps.

Consideration of Practicality, Feasibility, and Available Resources When Choosing a Thesis Topic

While aligning personal interests with research trends in security, learning, and cloud computing is crucial, it is equally important to consider the practicality, feasibility, and available resources when choosing a thesis topic. Here are some factors to keep in mind:

• Practicality: Ensure that your research topic on learning cloud computing can be realistically pursued within your PhD program’s given timeframe and scope.
• Feasibility: Assess the availability of necessary data, equipment, software, or other resources required for learning and conducting research effectively on cloud computing.
• Consider whether there are learning opportunities for collaboration with industry partners or other researchers in cloud computing.
• Learning Cloud Computing Advisor Expertise: Seek guidance from your advisor who may have expertise in specific areas of learning cloud computing and can provide valuable insights on feasible research topics.

Considering these factors, you can select a thesis topic that aligns with your interests and allows for practical implementation and fruitful collaboration in learning and cloud computing.

Identifying good research topics for a Ph.D. in computer science

Strategies for brainstorming unique ideas

Thinking outside the box and developing unique ideas is crucial when learning about cloud computing. One effective strategy for learning cloud computing is to leverage your personal experiences and expertise. Consider the challenges you’ve faced or the gaps you’ve noticed in your field of interest, especially in learning and cloud computing. These innovative research topics can be a starting point for learning about cloud computing.

Another approach is to stay updated with current trends and advancements in computer science, specifically in cloud computing and learning. By focusing on  emerging technologies  like cloud computing, you can identify areas ripe for exploration and learning. For example, topics related to artificial intelligence, machine learning, cybersecurity, data science, and cloud computing are highly sought after in today’s digital landscape.

Importance of considering societal impact and relevance

While brainstorming research topics, it’s crucial to consider the societal impact and relevance of your work in learning and cloud computing. Think about how your research in cloud computing can contribute to learning and solving real-world problems or improving existing systems. This will enhance your learning in cloud computing and increase its potential for funding and collaboration opportunities.

For instance, if you’re interested in learning about cloud computing and developing algorithms for autonomous vehicles, consider how this technology can enhance road safety, reduce traffic congestion, and improve overall learning. By addressing pressing issues in the field of learning and cloud computing, you’ll be able to contribute significantly to society through your research.

Seeking guidance from mentors and experts

Choosing the right research topic in computer science can be overwhelming, especially with the countless possibilities within cloud computing. That’s why seeking guidance from mentors, professors, or industry experts in computing and cloud is invaluable.

Reach out to faculty members who specialize in your area of interest in computing and discuss potential research avenues in cloud computing with them. They can provide valuable insights into current computing and cloud trends and help you refine your ideas based on their expertise. Attending computing conferences or cloud networking events allows you to connect with professionals with firsthand knowledge of cutting-edge research areas in computing and cloud.

Remember that feedback from experienced individuals in the computing and cloud industry can help you identify your chosen research topic’s feasibility and potential impact.

Tools and simulation in computer science research

Overview of popular tools for simulations, modeling, and experimentation.

In computing and cloud, utilizing appropriate tools and simulations is crucial for conducting effective studies in computer science research. These computing tools enable researchers to model and experiment with complex systems in the cloud without the risks associated with real-world implementation. Valuable insights can be gained by simulating various scenarios in cloud computing and analyzing the outcomes.

MATLAB is a widely used tool in computer science research, which is particularly valuable for computing and working in the cloud. This software provides a range of functions and libraries that facilitate numerical computing, data visualization, and algorithm development in the cloud. Researchers often employ MATLAB for computing to simulate and analyze different aspects of computer systems, such as network performance or algorithm efficiency in the cloud. Its versatility makes computing a popular choice across various domains within computer science, including cloud computing.

Python libraries also play a significant role in simulation-based studies in computing. These libraries are widely used to leverage the power of cloud computing for conducting simulations. Python’s extensive collection of libraries offers researchers access to powerful tools for data analysis, machine learning, scientific computing, and cloud computing. With libraries like NumPy, Pandas, and TensorFlow, researchers can develop sophisticated models and algorithms for computing in the cloud to explore complex phenomena.

Network simulators are essential in computer science research, specifically in computing. These simulators help researchers study and analyze network behavior in a controlled environment, enabling them to make informed decisions and advancements in cloud computing. These computing simulators allow researchers to study communication networks in the cloud by creating virtual environments to evaluate network protocols, routing algorithms, or congestion control mechanisms. Examples of popular network simulators in computing include NS-3 (Network Simulator 3) and OMNeT++ (Objective Modular Network Testbed in C++). These simulators are widely used for testing and analyzing various network scenarios, making them essential tools for researchers and developers working in the cloud computing industry.

The Benefits of Simulation-Based Studies

Simulation-based studies in computing offer several advantages over real-world implementations when exploring complex systems in the cloud.

• Cost-Effectiveness: Conducting large-scale computing experiments in the cloud can be prohibitively expensive due to resource requirements or potential risks. Simulations in cloud computing provide a cost-effective alternative that allows researchers to explore various scenarios without significant financial burdens.
• Cloud computing provides a controlled environment where researchers can conduct simulations. These simulations enable them to manipulate variables precisely within the cloud. This level of control in computing enables them to isolate specific factors and study their impact on the cloud system under investigation.
• Rapid Iteration: Simulations in cloud computing enable researchers to iterate quickly, making adjustments and refinements to their models without the need for time-consuming physical modifications. This agility facilitates faster progress in  research projects .
• Scalability: Computing simulations can be easily scaled up or down in the cloud to accommodate different scenarios. Researchers can simulate large-scale computing systems in the cloud that may not be feasible or practical to implement in real-world settings.

Application of Simulation Tools in Different Domains

Simulation tools are widely used in various domains of computer science research, including computing and cloud.

• In robotics, simulation-based studies in computing allow researchers to test algorithms and control strategies before deploying them on physical robots. The cloud is also utilized for these simulations. This approach helps minimize risks and optimize performance.
• For studying complex systems like traffic flow or urban planning, simulations in computing provide insights into potential bottlenecks, congestion patterns, or the effects of policy changes without disrupting real-world traffic. These simulations can be run using cloud computing, which allows for efficient processing and analysis of large amounts of data.
• In computing, simulations are used in machine learning and artificial intelligence to train reinforcement learning agents in the cloud. These simulations create virtual environments where the agents can learn from interactions with simulated objects or environments.

By leveraging simulation tools like MATLAB and Python libraries, computer science researchers can gain valuable insights into complex computing systems while minimizing costs and risks associated with real-world implementations. Using network simulators further enhances their ability to explore and analyze cloud computing environments.

Notable algorithms in computer science for research projects

Choosing the right research topic is crucial. One area that offers a plethora of possibilities in computing is algorithms. Algorithms play a crucial role in cloud computing.

PageRank: Revolutionizing Web Search

One influential algorithm that has revolutionized web search in computing is PageRank, now widely used in the cloud. Developed by Larry Page and Sergey Brin at Google, PageRank assigns a numerical weight to each webpage based on the number and quality of other pages linking to it in the context of computing. This algorithm has revolutionized how search engines rank webpages, ensuring that the most relevant and authoritative content appears at the top of search results. With the advent of cloud computing, PageRank has become even more powerful, as it can now analyze vast amounts of data and provide accurate rankings in real time. This algorithm played a pivotal role in the success of Google’s computing and cloud-based search engine by providing more accurate and relevant search results.

Dijkstra’s Algorithm: Finding the Shortest Path

Another important algorithm in computer science is Dijkstra’s algorithm. Named after its creator, Edsger W. Dijkstra, this computing algorithm efficiently finds the shortest path between two nodes in a graph using cloud technology. It has applications in various fields, such as network routing protocols, transportation planning, cloud computing, and DNA sequencing.

RSA Encryption Scheme: Securing Data Transmission

In computing, the RSA encryption scheme is one of the most widely used algorithms in cloud data security. Developed by Ron Rivest, Adi Shamir, and Leonard Adleman, this asymmetric encryption algorithm ensures secure communication over an insecure network in computing and cloud. Its ability to encrypt data using one key and decrypt it using another key makes it ideal for the secure transmission of sensitive information in the cloud.

Recent Advancements and Variations

While these computing algorithms have already left an indelible mark on  computer science research projects , recent advancements and variations continue expanding their potential cloud applications.

• With the advent of  machine learning techniques  in computing, algorithms like support vector machines (SVM), random forests, and deep learning architectures have gained prominence in solving complex problems involving pattern recognition, classification, and regression in the cloud.
• Evolutionary Algorithms: Inspired by natural evolution, evolutionary algorithms such as genetic algorithms and particle swarm optimization have found applications in computing, optimization problems, artificial intelligence, data mining, and cloud computing.

Exploring emerging trends: Big data analytics, IoT, and machine learning

The computing and computer science field is constantly evolving, with new trends and technologies in cloud computing emerging regularly.

Importance of Big Data Analytics

Big data refers to vast amounts of structured and unstructured information that cannot be easily processed using traditional computing methods. With the rise of cloud computing, handling and analyzing big data has become more efficient and accessible. Big data analytics in computing involves extracting valuable insights from these massive datasets in the cloud to drive informed decision-making.

With the exponential growth in data generation across various industries, big data analytics in computing has become increasingly important in the cloud. Computing enables businesses to identify patterns, trends, and correlations in the cloud, leading to improved operational efficiency, enhanced customer experiences, and better strategic planning.

One significant application of big data analytics is in computing research in the cloud. By analyzing large datasets through advanced techniques such as data mining and predictive modeling in computing, researchers can uncover hidden patterns or relationships in the cloud that were previously unknown. This allows for more accurate predictions and a deeper understanding of complex phenomena in computing, particularly in cloud computing.

The Potential Impact of IoT

The Internet of Things (IoT) refers to a network of interconnected devices embedded with sensors and software that enable them to collect and exchange data in the computing and cloud fields. This computing technology has the potential to revolutionize various industries by enabling real-time monitoring, automation, and intelligent decision-making in the cloud.

Computer science research topics in computing, including IoT and cloud computing, open up exciting possibilities. For instance, sensor networks can be deployed for environmental monitoring or intrusion detection systems in computing. Businesses can leverage IoT technologies for optimizing supply chains or improving business processes through increased connectivity in computing.

Moreover, IoT plays a crucial role in industrial computing settings, facilitating efficient asset management through predictive maintenance based on real-time sensor readings. Biometrics applications in computing benefit from IoT-enabled devices that provide seamless integration between physical access control systems and user authentication mechanisms.

Enhancing Decision-Making with Machine Learning

Machine learning techniques are leading the way in technological advancements in computing. They involve computing algorithms that enable systems to learn and improve from experience without being explicitly programmed automatically. Machine learning is a branch of computing with numerous applications, including natural language processing, image recognition, and data analysis.

In research projects, machine learning methods in computing can enhance decision-making processes by analyzing large volumes of data quickly and accurately. For example, deep learning algorithms in computing can be used for sentiment analysis of social media data or for predicting disease outbreaks based on healthcare records.

Machine learning also plays a vital role in automation. Autonomous vehicles heavily depend on machine learning models for computing sensor data and executing real-time decisions. Similarly, industries can leverage machine learning techniques in computing to automate repetitive tasks or optimize complex business processes.

The future of computer science research

We discussed the top PhD research topics in computing for 2024, provided guidance on selecting computing thesis topics, and identified good computing research areas. Our research delved into the tools and simulations utilized in computing research. We specifically focused on notable algorithms for computing research projects. Lastly, we touched upon emerging trends in computing, such as big data analytics, the Internet of Things (IoT), and machine learning.

As you embark on your journey to pursue a PhD in computing, remember that the field of computer science is constantly evolving. Stay curious about computing, embrace new computing technologies and methodologies, and be open to interdisciplinary collaborations in computing. The future of computing holds immense potential for groundbreaking discoveries that can shape our world.

If you’re ready to dive deeper into the world of computing research or have any questions about specific computing topics, don’t hesitate to reach out to experts in the computing field or join relevant computing communities where computing ideas are shared freely. Remember, your contribution to computing has the power to revolutionize technology and make a lasting impact.

What are some popular career opportunities after completing a PhD in computer science?

After completing a PhD in computer science, you can explore various career paths in computing. Some popular options in the field of computing include becoming a university professor or researcher, working at renowned tech companies as a senior scientist or engineer, pursuing entrepreneurship by starting your own tech company or joining government agencies focusing on cutting-edge technology development.

How long does it typically take to complete a PhD in computer science?

The duration of a Ph.D. program in computing varies depending on factors such as individual progress and program requirements. On average, it takes around four to five years to complete a full-time computer science PhD specializing in computing. However, part-time options may extend the duration.

Can I specialize in multiple areas within computer science during my PhD?

Yes! Many computing programs allow students to specialize in multiple areas within computer science. This flexibility in computing enables you to explore diverse research interests and gain expertise in different subfields. Consult with your academic advisor to plan your computing specialization accordingly.

How can I stay updated with the latest advancements in computer science research?

To stay updated with the latest advancements in computing, consider subscribing to relevant computing journals, attending computing conferences and workshops, joining online computing communities and forums, following influential computing researchers on social media platforms, and participating in computing research collaborations. Engaging with the vibrant computer science community will inform you about cutting-edge computing developments.

Are there any scholarships or funding opportunities available for PhD students in computer science?

Yes, numerous scholarships and funding opportunities are available for  PhD students  in computing. These computing grants include government agency grants, university or research institution fellowships, industry-sponsored computing scholarships, and international computing scholarship programs. Research thoroughly to find suitable options that align with your research interests and financial needs.

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PhD Assistance

How to select the right topic for your phd in computer science, introduction  .

Starting a PhD in Computer Science is an exciting but demanding effort, and choosing the correct computer science research topics is critical to a successful and rewarding experience. This critical decision not only influences the course of your academic interests, but also the effect of your contributions to the field. In this blog, we will look at crucial factors to consider when selecting a research subject, such as connecting with your passion, discovering gaps in current literature, and determining the feasibility of the project. By navigating this process with awareness and strategy, you will be able to begin a meaningful and effective doctorate research path in the dynamic field of computer science.  

• Check our PhD Topic selection examples to learn about how we review or edit an article for Topic selection.  

PhD in computer science is a terminal degree in computer science along with the doctorate in Computer Science, although it is not considered an equivalent degree. Computer science deals with algorithms and data and the computation of them via hardware and software, the principles and constraints involved in the implementation. Choosing a topic for research in computer science can be tricky. The field is as vast as its parent field, mathematics. Taking into account certain factors before choosing a topic will be helpful: it is preferable to choose a topic which is currently being studied by other fellow researchers, this will help to establish bonds and sharing secondary data. Finding a topic that will add value to the field and result in the betterment of existing processes will cement your legacy within the field and will also be helpful in getting funds. Always choose a topic that you are passionate about. Your interest in the topic will help in the long run; PhD research is a long, exhausting process and computational researches will dry you out. If you have an area of interest, read about the existing developments, processes, researches. Reading as much literature as possible will help you identify certain or several research gaps. You can consult with your mentor and choose a particular gap that would be feasible for your research. An extension of the previous method of spotting a research gap is to build on references for future research given in existing dissertations by former researchers. You can be critical of existing limitations and study it.

Besides, there are plenty of enigmatic areas in computer science. The unsolved questions within computer science plenty which you can study and find a solution to build on the existing body of knowledge. Major titles with unsolved questions for research in Computer Science

Computational complexity

The process of arranging computational process according to complexity based on algorithm has had various problems that are unsolved. This includes the Classic P versus the NP, the relationship between NQP and P, NP not known to be P or NP-complete, unique games conjecture, separations between other complexity cases, etc.

Polynomial versus non-polynomial time for specific algorithmic problems

A continuation in computational complexity is the complex case of NP- intermediate which contains within numerous unsolved problems related to algebra and number theory, Boolean logic, computational geometry, and computational topology, game theory, graph algorithm, etc.

Algorithmic problems

Scores of questions within the existing algorithm in computer science can be improved with new processes.

Natural Language Processing algorithms

Natural language processing is an important field within computer science with the onset of deep learning and Artificial and Intelligence. Plenty of researches are being carried in the field to find faster and perfect ways to syllabify, stem, and POS tag algorithms specifically for the English language.

Programming language theory

The case for scope of research about programming language within computer science is evergreen. There are always ways to design, implement, analyze, characterize, and classify programming languages and to develop newer languages.

• Check out our study guide to learn more about How to Select the Best Topics for Research?  

Conclusion:  

In conclusion, the journey of selecting the right PhD topic in computer science topics is a pivotal phase requiring careful deliberation. By combining passion, alignment with current computer science phd topics trends, and feasibility assessment, one can pave the way for a successful and rewarding research endeavor. Remember, the chosen topic will not only define your academic trajectory but also contribute to the evolving landscape of computer science thesis topics. Embrace the challenge with purpose, stay adaptable, and ensure that your research aligns with both personal interests and the broader needs of the field. With these considerations, you are poised to make a lasting impact in the world of Computer Science.  

Example Research Topics in Technology and Computer Science    

• Role of human-computer interaction   
• AI and robotics   
• Software engineering and programming   
• Machine learning and neuron networks  

About PhD Assistance  

At PhD Assistance , we have a team of trained research specialists with topic selection experience. Our writers and researchers have extensive expertise in selecting the appropriate topic and title for a PhD dissertation based on their Specialized subject and personal interests. Furthermore, our professionals are drawn from worldwide and top-ranked colleges in nations such as the United States, United Kingdom, and India. Our writers have the expertise and understanding to choose a PhD research subject that is actually excellent for your study, as well as a snappy title that is unquestionably appropriate for your research aim.  

In summary, it is important to keep in mind the following to choose an apt topic for your PhD research in Computer Science:

Your passion for an area of research

Appositeness of the topic

Feasibility of the research with respect to the availability of the resource

Providing a solution to a practical problem.

Topic selection help for computer science students  

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Best Doctorates in Computer Science: Top PhD Programs, Career Paths, and Salaries

Getting a PhD in the field of computer science is the best way to influence the future of technological innovation and research. If you are interested in getting a computer science doctoral degree, then our list of the best PhDs in Computer Science will help you find the program that caters most to your goals.

A PhD in Computer Science can branch out into a wide variety of science and tech fields. Be it information assurance, computational science theory, or cyber operations, you can specialize your computer science PhD to suit your interests. In our guide, we’ve also gone into detail about the average PhD in Computer Science salary and the best computer science jobs PhD students can get.

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What is a phd in computer science.

A PhD in Computer Science is a doctoral degree where graduate students perform research and submit original dissertations covering advanced computing systems topics. Computer science is a broad field that covers artificial intelligence, operating systems, software engineering, and data science.

Your doctoral dissertation will include a research proposal, coursework in advanced topics related to computer science, and a thesis presentation. The wide span of this field allows you to choose a PhD program that can cover topics in any high-performance computing systems area.

How to Get Into a Computer Science PhD Program: Admission Requirements

The admissions requirements to get into a computer science PhD program include submitting your official transcripts from your undergraduate or graduate programs and resume. Your previous university coursework should showcase a strong background in software development, popular programming languages , and scientific computing.

Universities also usually require the submission of your GRE score. A combined score of 1,100 is typically where you want to be when applying to PhD programs. You’ll also usually be required to submit three or more letters of recommendation and a personal essay stating your thesis or research proposal. Keep in mind that each university’s admissions requirements will vary.

PhD in Computer Science Admission Requirements

• 3.0 or higher cumulative GPA
• Three letters of recommendation
• Official transcript from your undergraduate degree or your graduate degree
• Prerequisite courses covering computer science academic programs
• Personal statement highlighting proposal of thesis or research topic

Computer Science PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Computer Science?

It is very hard to get into a PhD program in computer science. This is because prospective students need to meet a very competitive GPA, have an excellent academic background, and fulfill other advanced program requirements. Your chances of getting accepted into a computer science doctorate degree program will typically range between 10 to 20 percent.

In fact, less than 10 percent of computer science graduate applicants are accepted at the University of California. Similarly, Duke University reports that only around 15.7 percent of applicants were selected for its 2021 to 2022 computer science PhD program. Your acceptance relies on submitting a compelling thesis proposal statement that displays your passion and high academic competency.

How to Get Into the Best Universities

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Best PhDs in Computer Science: In Brief

Best universities for computer science phds: where to get a phd in computer science.

The best universities for computer science PhDs are Arizona State University, Boston University, Harvard University, Duke University, and Carnegie Mellon University. Each of these universities will help you advance your research and eventually get you a job in artificial intelligence , software development, or computing systems. We’ve also broken down the application process and other details for each program.

According to the US News & World Report, Arizona State University ranks number one on the list of the most innovative schools and number 36 in the best undergraduate engineering programs. It was founded in 1885 and currently offers over 450 graduate programs and employs more than 340 PhD fellows. 

PhD in Computer Science 

Arizona State University offers research opportunities in the fields of artificial intelligence, cyber security, big data, or statistical modeling under the umbrella of this computer science program. In this 84-credit program, you’ll tackle your dissertation, prospectus, and oral and written exams. You’ll also take courses on computational processes, information assurance, and network architecture. 

Your PhD dissertation includes 12 credit hours of experience culmination that can be planned alongside your research and elective credits. This degree is best suited for computer scientists wanting to build a career in machine learning or an academic career. 

PhD in Computer Science Overview

• Program Length: 4 to 6 years
• Acceptance Rate: N/A
• Tuition and Fees: $6,007/semester, nine credits or more (in state); $1,663/hour, under 12 credits or $16,328 per semester, 12 credits or more (out of state) 
• PhD Funding Opportunities: Teaching assistantships, research assistantships
• Three letters of recommendations from former professors or employers 
• One to two-page statement of purpose that covers previous research experiences and reasoning behind your interest in one to two doctoral programs
• Optional submission of GRE scores. Preferred scores are 146 verbal, 159 quantitative, and 4.0 analytical writing
• Official transcripts
• Bachelor’s Degree in Computer Science or computer engineering. Applicants with a master’s degree in a relevant field are preferred 
• Minimum 3.5 cumulative GPA

Founded in 1839, Boston University is a top private research university with a reputable engineering and technology program. It offers over 350 graduate programs and PhDs in topics such as neurobiology, biostatistics, computer engineering, mathematical finance, and systems engineering. 

PhD in Computer Science

If you are interested in advancing in research and academia, then this PhD program is worth looking into. Its curriculum trains you to build a successful professional background in the intelligent control systems, cloud infrastructures, and cryptography fields. Candidates need to clear its qualification, dissertation, and milestone requirements to complete this degree. 

• Program Length: 5 to 6 years
• Acceptance Rate: 10%
• Tuition and Fees: $61,924/year
• PhD Funding Opportunities: Computer Science Fellowship, Teaching Excellence Award, Research Excellence Award, Teaching Fellow Expectations 
• GRE scores normally mandatory, but are optional for fall 2022
• A personal statement stating your interest in the program 
• Resume 

Carnegie Mellon University is a globally recognized university with more than 14,500 students and over 109,900 alumni. The school was founded in the year 1900 and offers over 80 majors and minors. According to the US News & World Report, Carnegie Mellon University ranks number one on the best undergraduate computer science program in the country. 

This on-campus PhD program focuses on computing research, software informatics, and communication technologies. Completing this doctoral degree program will open you up to a wide range of career prospects across the data science, computing technology, and information technology research fields. 

This degree includes 24 units of advanced computing research, 72 units of graduate courses, and the dissertation process of an original research thesis. This PhD is apt for those looking to establish their career in research and academia. During this program, you’ll also serve as a teaching assistant in the computer science department twice as per the degree requirement. 

• Acceptance Rate: 5% to 10%
• Tuition and Fees: $75,272/year 
• PhD Funding Opportunities: Internal funding, external funding, dependency allowance, fellowships
• GRE scores optional but encouraged
• Most recent transcript of the university attended
• One to two-page statement of purpose stating your interest in the program, research interests, PhD objective, and relevant experience
• Three letters of recommendation from previous faculty or employers   

Duke University was established in 1924 and counts among the top universities in the world. It has an undergraduate population of 6,789 and a graduate population of 9,991 students and is most recognized for its computer science, biology, public policy, and economics departments. It offers over 80 doctoral and master’s degrees covering STEM, social sciences, and humanities. 

This computer science PhD is definitely worth it for doctorate students looking to embark on an advanced computer science research path. In it, students tackle a research initiation project, preliminary exam, dissertation process, and core qualification credits. Doctoral candidates are also required to partake in the department’s teaching assistantship program. 

Its curriculum includes core courses in computation theory, artificial intelligence, algorithms, numerical analysis, and computer architecture. Graduates of the program open themselves up to numerous career opportunities across a wide range of computing systems academic and research fields. 

• Program Length: 3 to 4 years
• Acceptance Rate: 15.7%
• Tuition and Fees: $70,185/year for the first three years and $18,165/year each subsequent year
• PhD Funding Opportunities: Teaching assistantships, research assistantships, fellowships
• Official transcripts from all attended universities 
• Statement of purpose
• GRE scores are optional for 2022 but recommended 
• No minimum GPA requirements but high GPA scores are preferred

Harvard University is a top Ivy League institution that has amassed global recognition and top rankings in many of its departments. Founded in 1636, the university is home to many excellent programs across the fields of law, medicine, economics, and computer science. It has more than 400,000 alumni and a total enrollment of 35,276 students. 

According to the US News & World Report, Harvard University ranked number one among the best global universities in 2022 . Its graduate schools offer doctorate programs in the applied sciences, biology, literature, environmental sciences, business, and healthcare fields. 

Attending a computer science PhD program at Harvard University brings high credibility and accolades to your professional candidacy. This program is offered by the university’s Graduate School of Arts and Sciences and provides focus opportunities across the engineering science, applied physics, computer science, and applied mathematics areas.  

Similar to most mainstream PhDs, this program requires the completion of 10 semester-long graduate courses, a dissertation topic, oral and written qualifying exams, a teaching assistantship, and a defense process. After graduating, you’ll easily qualify for some of the most prestigious research and career opportunities available.

• Program Length: 3 or more years
• Acceptance Rate: 6%
• Tuition and Fees: $50,928 for the first two years and $13,240 reduced tuition for the third and fourth year
• PhD Funding Opportunities: Teaching fellowships, research assistantships, GSAS fellowships, external funding 
• Supplemental form for PhD
• Transcripts from all post-secondary education 
• Statement of purpose stating your interest in the program  

Oregon State University is a public research university founded in 1868 with over 210,000 alumni. The school is home to more than 28,607 undergraduate and 5,833 graduate students and offers over 300 academic programs as well as a robust research department. Its doctoral programs can be found in the business, agricultural science, education, engineering, or medicine departments. 

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This PhD is offered by the university’s electrical engineering and computer science department and is perfect for doctoral candidates wanting to work in IT research in the governmental or educational sectors. The program offers research opportunities in topics such as data science, cyber security, artificial intelligence, computer graphics, and human-computer interaction. 

The program’s curriculum includes graduate-level courses in theoretical computer science and requires the completion of your research thesis. You’ll also be required to maintain an overall cumulative GPA of 3.0 and pass all preliminary and oral exams to receive your PhD. 

• Program Length: 4 years
• Tuition and Fees: $557/credit (in state); $1,105/credit (out of state)
• PhD Funding Opportunities: Graduate teaching assistantship, research assistantship, Outstanding Scholars Program
• Three letters of recommendation from previous professors or employers familiar with your technical skills 
• Transcripts and academic history of all attended universities 
• Minimum 3.0 GPA in the last two years of your undergraduate or graduate work 
• Statement of objective listing your interest in the program, career goals, research interests, and relevant experience

Syracuse University is a private institution that was established in 1870 and is most popular for its research and professional training academic programs. It has more than 40 research centers focusing on the STEM, social sciences, and humanities fields. The university has over 400 majors, minors, and advanced degrees its students can choose from. 

It had a total enrollment of 14,479 undergraduate students and 6,193 graduate students in the fall of 2020. Prospective students can pick a PhD focus from many of its applied topics, including data science, statistics, human development, and bioengineering. 

PhD in Computer and Information Science and Engineering

A PhD focused in computer and information science and engineering from Syracuse University can help you advance your career in the information technology, software engineering, or information assurance fields. This program is best suited for computing technology research buffs looking to land senior-level positions in the field. 

The program’s curriculum is an amalgamation of graduate coursework, your dissertation and research presentation, and exams. Your coursework will cover technical topics ranging from algorithms and artificial intelligence to operating systems and hardware systems. 

PhD in Computer and Information Science and Engineering Overview

• Program Length: 4 to 5 years
• Acceptance Rate: 14.28%
• Tuition and Fees: $32,110/year 
• PhD Funding Opportunities: Research assistantships, departmental teaching assistantships, university fellowships

PhD in Computer and Information Science and Engineering Admission Requirements

• Minimum GRE scores: Verbal 153, Quantitative 155, and analytical writing 4.5 
• Bachelor of Science or Master of Science in computer engineering, electrical engineering, or computer and information science
• Two or more letters of recommendation from previous faculty or employers 
• Official transcripts of all attended universities 
• 500-word personal statement concerning your interest in the program

The University of Oklahoma is a public school best known for its business, journalism, and petroleum engineering programs. Founded in 1890, it currently has an undergraduate student population of 21,844 and offers over 170 academic programs and graduate degrees in a wide range of subject areas. 

The school’s doctoral topics are numerous and can be found within its business, architecture, fine arts, education, engineering, journalism, or geographics science departments. The University of Oklahoma is also incredibly well known for its athletic programs, having won many national championships.

The university’s computer science PhD has courses in machine learning, data science, computer security, visual analytics, database management, and neural networking subjects. If you’re interested in a data science, network security, artificial intelligence, or cyber security career, then this PhD is for you.

The program allows you to propose a research topic covering anything in the field of advanced computing systems and theories. During your program, you’ll undergo an annual research progress review along with general examinations until your defense. The program also requires you to submit a minimum of two publications before you complete your degree. 

• Program Length: 6 years
• Tuition and Fees: $591.90/credit (in state); $1,219.50/credit (out of state)
• PhD Funding Opportunities: Graduate assistantships, research assistantships, fellowships, scholarships, research grants
• Prerequisite coursework covering computer science, data structures, and math subjects 
• Bachelor’s degree or master’s degree
• Minimum cumulative 3.0 GPA 
• 250-word statement of purpose concerning your interest and goals in the program 
• Three letters of recommendation, with two of them preferably from previous professors

The University of Arizona was founded in 1885 and is a public research institution with over 300 major programs. The school is home to 36,503 undergraduate and 10,429 graduate students and offers PhD programs in over 150 areas of study, including information science, statistics, mechanical engineering, biomedical science, medicine, communication, and economics. 

If you want to become an applications architect or pursue a career in academia focusing on computing or business intelligence technologies, then this PhD is for you. It offers courses in computer networking, system architecture, database systems, machine learning theory, natural processing language, and computer vision. 

The program’s curriculum requires the completion of 12 units of advanced computer science research and 18 units of dissertation presentation and defense. You’ll also need to maintain a minimum cumulative GPA of 3.33 to receive your PhD. 

• Program Length: 5.5 years
• Acceptance Rate: 17.73%
• Tuition and Fees: $989.12/unit (in state); $1,918.12/unit (out of state)
• PhD Funding Opportunities: Graduate assistantships, graduate associate fund, teaching assistantships, research assistantships, graduate college fellowship
• Official transcripts from all attended universities
• Minimum of two letters of recommendation by previous faculty or employers 
• A statement of purpose stating your interest in the school and the program faculty, your career goals, preferred research areas, and research background
• Resume detailing previous research work, published papers, conference presentations, and computer science background 
• Bachelor’s degree in computer science or a related field 
• A background in operating systems, programming languages, discrete mathematics, data structures, and theory of computation 
• Minimum 3.5 undergraduate GPA and 3.7 graduate GPA 

The University of Maryland is a research-focused institution that was founded in 1856. It hosts more than 41,200 students and offers over 217 undergraduate and master’s programs. It also offers 84 doctoral programs and has an extensive research department. According to the US News & World Report, the school ranks number 20 among the top public schools in the country .

This PhD program offers research opportunities in subjects such as robotics, big data, scientific computing, machine learning, geographic information systems, and quantum computing. Doctoral students can participate in a collaborative research journey at any of the school’s research specialized institutions. The program curriculum includes graduate coursework, a research proposal, and a dissertation defense. 

• Tuition and Fees: $11,586/year (in state); $24,718/year (out of state) 2022-2023
• PhD Funding Opportunities:  Research assistantships, departmental teaching assistantships, National Science Foundation Graduate Fellowships, Fulbright Fellowships
• Transcripts from all attended universities
• Writing sample and optional publications or presentations 
• Statement of purpose concerning your interests in the field and program 
• Three letters of recommendation 

Can You Get a PhD in Computer Science Online?

Yes, you can get a PhD in Computer Science online. An online doctoral degree will be more course-based instead of research-based due to the lack of laboratory facilities. Computer science is a broad field that offers doctoral opportunities across a wide range of tech topics. You can get an online PhD in information science, data science, data analytics, or information systems.

Know that online PhDs are rare across most fields, including computer science. Obtaining a non-research-focused doctoral degree won’t be as respected as a traditional computer science PhD. The online PhD programs listed below are best suited for candidates looking to advance into managerial, theoretical research, and academic positions in the technology sector.

Best Online PhD Programs in Computer Science

How long does it take to get a phd in computer science.

It takes an average of four years to get a PhD in Computer Science. However, the actual duration is entirely dependent on the candidate’s research proposal approval and defense success, and depending on your research pace, it can take up to five or six years to complete. The graduate course portion of your degree is the most straightforward and typically takes around 2.5 years to complete.

Your dissertation topic selection, research journey, publication submissions, and defense presentations will take the most amount of time, usually between three to five years. Some universities also require their PhD students to complete a minimum of two years of graduate teaching assistantship. An online PhD in Computer Science usually only takes three years to finish, as it mostly includes advanced coursework.

Is a PhD in Computer Science Hard?

Yes, a PhD in Computer Science is hard. Computer science is a complex field that incorporates an array of advanced technical topics. Your PhD will require you to submit an original research proposal on an advanced information technology subject such as data science, machine learning, quantum computing, artificial intelligence, and network security topics.

Along with advanced research and a dissertation, you’ll also need to complete advanced graduate courses with a minimum GPA of 3.0. Other requirements often include submitting one or more publications, working in graduate teaching positions, and successfully defending your thesis topic. The combination of all of these academic requirements makes getting a PhD in Computer Science a hard process.

How Much Does It Cost to Get a PhD in Computer Science?

It costs $19,314 per year to get a PhD in Computer Science, according to the National Center for Education Statistics (NCES). However, your total PhD tuition can vary depending on a number of factors, including the university’s ranking, the program’s timeline, and the PhD funding opportunities you’ll have available.

The NCES further categorizes the graduate program tuition according to the institution type and reports that the average fee for public institutions was $12,171 from 2018 to 2019. It also states that private for-profit institutions charged an average of $27,776, and non-profit schools charged $14,208 those same years.

How to Pay for a PhD in Computer Science: PhD Funding Options

The PhD funding options that students can use to pay for a PhD in Computer Science include graduate research assistantships, teaching assistantships, and fellowship opportunities. Your funding options will vary from school to school and can include both external and internal funding.

Some of the popular ways to fund your PhDs include research grants, federal work-study programs, teaching or graduate assistantships, tuition waivers, and graduate research fellowships. You can also apply for scholarships or tuition reimbursement options at your current job. Your graduate advisor and computer science faculty can help you find more funding options.

Best Online Master’s Degrees

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What Is the Difference Between a Computer Science Master’s Degree and PhD?

The difference between a computer science master’s degree and a PhD is the level of each degree. A Master’s Degree in Computer Science is a typical precursor to a PhD and covers the technical field less extensively than a doctoral program. It will last around two to three years and can be fully course-based or thesis-based.

A PhD in Computer Science provides you with higher qualifications and more research and dissertation autonomy. It can last anywhere between four to six years and gives you original publication and research credibility. Both of these computer science degrees are considered graduate degrees, but a PhD provides you with a higher educational accolade.

Master’s vs PhD in Computer Science Job Outlook

The job outlook for a professional with a master’s vs PhD in Computer Science will generally coincide as most senior-level careers can be achieved with a master’s degree. According to the US Bureau of Labor Statistics (BLS), the job outlook for computer and information research scientists is projected to grow by 22 percent between 2020 and 2030.

This job typically requires a master’s degree meaning PhD holders also qualify and can apply for it. The commonality of these job growth statistics also applies to other tech positions, including information security scientists and network architects. That being said, the specific growth rate of your job will also vary depending on your career choice.

For example, university computer science professor positions, which typically only computer science PhD holders are eligible for, have a projected growth rate of 12 percent between 2020 and 2030, according to the BLS. With computer science professionals being high in demand, most PhD in Computer Science jobs have a positive projected growth rate.

Difference in Salary for Computer Science Master’s vs PhD

The difference in salary for computer science master’s vs PhD grads can vary depending on their position and place of employment. According to PayScale, the average salary for a computer science PhD holder is $131,000 per year , which is higher than the average salary of a master’s degree graduate.

According to PayScale, the average salary for a computer science master’s graduate is $105,000 per year . The salary disparity with these degrees stems from the differences in their level of seniority, industry experience, and educational accolades.

Related Computer Science Degrees

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Why You Should Get a PhD in Computer Science

You should get a PhD in Computer Science because it is an advanced and highly reputable degree that will help you land senior technical, academic, and research roles. A PhD is a gateway to a lucrative and innovative technology career, allowing you to follow your research passion across the fields of artificial intelligence, data science, or computing theory.

Reasons for Getting a PhD in Computer Science

• Extensive and advanced research opportunities. A PhD in Computer Science covers many advanced computing science fields. You can learn specialized skills through your research opportunities and eventually work in advanced data science, artificial intelligence, neural networking, information technology, or computing theory.
• Higher salary. PhD graduates qualify for career opportunities working in senior positions as scientists, professors, managers, or heads of departments. These senior positions come with high compensation and job security.
• Rewarding education. A computer science PhD is perfect for those who are interested in contributing toward leading innovation and technology research. As a doctoral student, you can propose and conduct advanced research in the field while contributing to today’s technological growth.
• Increased job candidacy. Having a computer science PhD on your resume and portfolio will enhance your candidacy when applying to tech positions across all industries. A PhD is a highly reputable degree that demonstrates your expertise in the field and ultimately makes you a highly sought-after candidate.

Getting a PhD in Computer Science: Computer Science PhD Coursework

The graduate requirements for getting a PhD in Computer Science and most common PhD coursework are different from program to program and are heavily dependent on your specialization, but often have some commonalities. Here are some examples of courses you may take during your PhD.

System Architecture

A systems architecture course in a computer science PhD covers advanced operating systems, communication technologies, network security, and computer architecture. You’ll also take classes covering topics like network systems and software engineering.

Artificial Intelligence

Artificial intelligence is a rapidly growing field that is integral to the field of computer science and data science. Your program will cover the latest artificial intelligence technologies and research areas such as deep learning, interactive systems, neural networking, and artificial intelligence infrastructure.

Information Assurance

Network security, information assurance, and cyber security are also part of an extensive education coverage of the computer science field. This course will cover vital knowledge concerning information security, system integrity, data privacy, and system authentication.

Data science courses in a computer science PhD program cover topics such as big data, database management, data analytics, data mining, and machine learning subjects. You will learn about data science processes and methods as well as the tools and technologies used in advanced data engineering.

Theory of Computation

A theory of computation course will teach you advanced algorithms, computation models, Turing machines, quantum computing, and automata theories. You’ll also have lessons that cover the Godel Incompleteness theorem and molecular computing.

Best Master’s Degrees

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How to Get a PhD in Computer Science: Doctoral Program Requirements

If you are wondering how to get a PhD in Computer Science and complete the doctoral program requirements, this section will provide you with the answers you’re looking for. The graduation and academic requirements will vary from one PhD program to another, but there are some common requirements across all computer science departments. Here are some of them.

A computer science PhD is an amalgamation of graduate-level courses and research. All PhDs will require you to complete their graduate course requirements which cover topics like data science, computing systems, artificial intelligence, and information assurance. The required number of courses will vary depending on the program but is typically between 10 and 15. 

Maintaining a minimum required cumulative GPA in your courses is a requirement across all PhD programs. The GPA requirement can range anywhere from 3.0 to 3.5. This is one of the major ways your program department tracks your progress and whether or not you are struggling with the work.

Clearing the qualifying exams with a passing grade while maintaining the required GPA is another PhD graduation requirement. Your preliminary exam is a public presentation discussing your research topics with approval committees and other students. Written exams and oral exams come with each course and are a test of your computer science and tech abilities.  

You are typically required to present your research proposal or research initiation project within the first two years of your PhD. You must get your research idea approved by the approval committee and begin the research process within those two years. 

Once you embark on your computer science research process, you are required to present an annual progress report. This presentation is a review process where the approval committee will ask questions and provide feedback on your progression.  

Your PhD milestones may also include publication requirements. For these, you’ll be required to submit one or two peer-reviewed journal or publication entries covering the computer science topics you are researching. 

Universities also require PhD candidates to complete two years of graduate teaching assistantships or research assistantships. These assistantships are one of the best ways to secure funding for your PhD program. 

Getting your dissertation approved and completing your research and thesis is one of the most important milestones of your PhD. Your assigned research committee, thesis advisor, and approval committee will need to approve your research and dissertation for your to be able to graduate. 

Computer science PhDs will have a timeline breakdown that candidates are expected to meet. You will typically need to complete the graduate coursework within two to three years and complete your dissertation and thesis within six years. You can request a timeline extension with your advisor’s approval.

The thesis for your PhD in Computer Science will cover your chosen research subject area. It will include a thesis proposal submission, thesis presentation, and thesis approval process as well as an extensive written document covering your hypothesis, findings, and conclusions. 

Potential Careers With a Computer Science Degree

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PhD in Computer Science Salary and Job Outlook

The salary and job outlook for a PhD in Computer Science will vary according to your job designation but are generally positive. The average salary for some of the highest-paid jobs will range between $86,712 and $179,351. Below are some of the most lucrative career paths a computer science PhD holder can embark on.

What Can You Do With a PhD in Computer Science?

You can work in a wide range of advanced technical positions with a PhD in Computer Science. This doctoral degree qualifies you for positions as a manager, scientist, college professor, and researcher. You could lead an information assurance department or become a computer science professor, chief data scientist, or artificial intelligence researcher.

Best Jobs with a PhD in Computer Science

• Computer Research Scientist
• Computer Science Professor
• Research and Development Lead
• Computer Systems Engineer
• Information Technology Manager

What Is the Average Salary for a PhD in Computer Science?

The average salary for someone with a PhD in Computer Science is $131,000 per year , according to PayScale. Your actual salary will vary depending on your specific position, location, and experience. In fact, with a PhD, you could work as a chief data scientist and make between $136,000 and $272,000 or as a senior software engineer and make $104,000 to $195,000.

Highest-Paying Computer Science Jobs for PhD Grads

Best computer science jobs with a doctorate.

The best computer science jobs with a doctorate degree all earn a high salary and have high projected growth in the next few years. These jobs cover a wide range of computer science disciplines, meaning that you’ll easily be able to find a position doing something you enjoy.

A chief data scientist is in charge of the data analytics and data science departments of an organization. They are responsible for the approval of new database system designs, data strategies, and data management decisions. 

• Salary with a Computer Science PhD: $179,351
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, Washington

A chief information officer is an IT executive responsible for managing and overseeing the computer and information technology departments of a company. Also known as CTOs, they are responsible for delegating tasks and approving innovation and technology upgrade ideas proposed by their teams. 

• Salary with a Computer Science PhD: $168,680
• Job Outlook: 11% job growth from 2020 to 2030
• Number of Jobs: 482,000
• Highest-Paying States: New York, California, New Jersey, Washington, District of Columbia

A senior computer scientist heads the research department of a computer science, artificial intelligence, or computer engineering field. These professionals, along with their research team, are tasked with developing efficient and optimal computer solutions across a wide range of sectors. 

• Salary with a Computer Science PhD: $153,972

An IT security architect is a cyber and information security professional responsible for developing, maintaining, and upgrading the IT and network security infrastructure of a business or organization. Additionally, they oversee an organization’s data, communication systems, and software systems security aspects. 

• Salary with a Computer Science PhD: $128,414
• Job Outlook : 5% job growth from 2020 to 2030
• Number of Jobs: 165,200
• Highest-Paying States: New Jersey, Rhode Island, Delaware, Virginia, Marlyand

A computer science professor is a university professor who educates college students concerning basic and advanced computer science subjects. They are responsible for creating and instructing a course curriculum as well as testing their students. Some computer science professors also work as research faculty at a university. 

• Salary with a Computer Science PhD: $86,712
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900 
• Highest-Paying States: California, Oregon, District of Columbia, New York, Massachusetts

Is a PhD in Computer Science Worth It?

Yes, a PhD in Computer Science is worth it for anyone wanting to work in senior professions in the field of technology. This doctoral degree opens its recipients up to numerous career opportunities across academia, research and development, technology management, and chief technical positions.

Getting a computer science PhD equips you with specialized skills and extensive research capabilities. During your studies, you’ll get the opportunity to contribute to the rapidly developing world of technology with your original dissertation and specialize in data science, network security, or computing systems.

Additional Reading About Computer Science

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PhD in Computer Science FAQ

The preferred GPA for a computer science PhD is 3.5 or above. Keep in mind that meeting the minimum requirement doesn’t guarantee acceptance. The higher you can get your GPA during your bachelor’s and master’s, the more likely it is you will be accepted to the PhD program of your choice.

The standardized exam you need to take to get a PhD in Computer Science is the Graduate Record Examination (GRE). The GRE score requirements will vary from university to university and several schools have currently waived GRE requirements due to the coronavirus pandemic.

You can choose from a wide range of potential research subjects for your computer science PhD, including computer algorithms, data science, artificial intelligence , or cyber security. You can also research business process modeling, robotics, quantum computing, machine learning, or other big data topics.

You can get into a computer science PhD program by impressing the admissions committee and the school’s computer science graduate department with your skills, experience, grades, and desired research topic. Students with a 3.5 or higher GPA, a high GRE score, extensive IT skills, and an impressive research topic have a higher chance of admission.

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Tips to Become a Better (Computer Science) Ph.D. Student

Why does the world need another blog post.

There are already a lot of great blogs posts about the computer science Ph.D. experience, each approaching it from a different angle (the whole process of a Ph.D., how to choose your research topic, etc.). However, the ideas presented in most of these blog post come from the experience of one person while this blog is a condensed summary of in-depth talks with more than five professors and three Ph.D. student during the YArch workshop at HPCA’19. During these conversations, we discussed topics that are important for early year computer science Ph.D. students . We chose ten ideas we found most impactful to us, and explain five of them in detail and present the other five as short tips.

Research > Courses

Be professional, read a lot and read broadly, impact humankind, don’t give up on your research topic easily, aim for top-tier conferences.

• Use existing resources in your groups

You are powerful!

Focus on publishing.

If you have more ideas, please comment at the bottom of this post!

Other amazing blogs out there:

• The Ph.D. Grind
• Tips: How to Do Research
• So long, and thanks for the Ph.D.!
• Graduate School Survival Guide
• Tips for a New Computer Architecture PhD Student

Young Ph.D. students tend to spend too much time on courses. However, research outweighs courses.

Take courses with a grain of salt

Courses are not as important as they seem to be. The priority of a Ph.D. student is to do research – the earlier you start your research, the better off you’ll be in the long run.

However, don’t go to extremes ! A poor grade can also be a huge problem. You should always be familiar with the requirement of qualification exams or generals and meet all the standards about the courses.

Remember the main ideas of courses

Trapping ourselves in trivial details of a course is easy. However, most of the specifics are not important to our research even if the topic is related to our area.

A good approach is to use what you’ve learned from one course and apply it to a different field (e.g., taking an analysis tool from a compiler course and applying it in computer networks).

Treat your Ph.D. as a job. You get paid (albeit not much) for being a Ph.D. candidate, so make your work worth the money. This professional mindset should also be apparent to your advisor. Some advisors take on a more hands-off approach, for instance letting you work from home, but this is no reason for slacking; you should be responsible for your research schedule, such as reminding your advisor of plans from previous group meetings. Your status is not that of a student but rather that of a peer in the research community.

Though it can be very daunting starting out, reading papers is an essential part of the Ph.D. life. Previously, you may have read papers when it was necessary for a class or a project. However, you should put reading papers in your daily routine. Doing so allows you to draw inspiration from a sea of knowledge and prevents yourself from reinventing the wheel. Besides, it’s a great way to be productive on a slow day.

Make a plan to read

When scheduling your day, assign one period just for reading papers. You can read one paper in depth or compare several papers; regardless of your choice, allotting time to this task is the key.

Read broadly

Reading papers from different subfields of computer science is a great way to learn the jargon, the method, and the mindset of researchers in each field. This can be the first step towards discovering opportunities for collaboration.

It is not uncommon for a Ph.D. student to spend several years building a system that turns out to be fundamentally flawed or not as applicable as expected. Don’t worry! There is nothing wrong with failing, and perhaps we should even expect failure to be part of the journey. But we should aim to fail early in order to have time to work on another project (and graduate!).

Perform a limit study

Perform a quick limit study before sticking with a project. A limit study includes in-depth analyses of implicit assumptions we make when coming up with an idea, a related works search, and the potential of the work if everything goes well. A great limit study can itself be a publishable paper. An example can be found here .

Hacky implementation can be useful

Being a researcher, your work is to develop proof-of-concepts. Nevertheless, you need to demonstrate that your concept is sound for the simplest of cases before continuing to the full-blown system. Hack in the minimum set to show that your idea is possible while resisting the temptation to build a robust infrastructure – if your idea fails, you will know to stop earlier.

Impacting humankind may sound too ambitious, but it should be the ultimate reason why we embark on this journey.

Choose an impactful research topic

In terms of how our Ph.D. research could impact human knowledge, I would like to refer to The Illustrated Guide to a Ph.D. by Matt Might. All we will do in five years is pushing the boundary of human knowledge by a minute margin. Choose a topic that you are able to contribute to, feel passionate about, and can explain the importance of to a layman in a 3-min talk.

Check out why Matt Might changed his research focus from programming languages to precise medicine.

How can our research actually impact people from other fields?

A survey paper by the Liberty Research Group sheds light on how the improvement of programming tools impacts ( computational scientists ) all scientists. Thinking about how your research affects people from other fields can help you define the scope of your contribution.

At some point, we will get bored with our research topic and find something else interesting. Think twice before switching topics. You must differentiate between your project heading nowhere and you getting tired of being stuck.

You should focus on publishing at only top-tier conferences. Don’t consider second-tier venues unless the work has been rejected several times by top-tier conferences. This can prevent you from doing incremental work to make your publication list look better.

Use existing resources in your group

For many fields in computer science, a mature infrastructure requires several years of development by multiple graduate students. Think about how to make use of the infrastructure and resources in the group to boost your research progress.

Even though we are just junior graduate students, we can have a massive impact on ourselves, our group, and even our department. For example, if there is no reading group for your field in your department, start one!

Needless to say, publications are essential since those are what people look at once we graduate.

Acknowledgment

All the ideas in this blog originate from the talks with mentors of the YArch’19 workshop. Thanks to Prof. Boris Grot from the University of Edinburgh, Prof. Thomas Wenisch from the University of Michigan, Prof. Vijay Janapa Reddi from Harvard University, Prof. Luis Ceze from the University of Washington, and Prof. Kevin Skadron from the University of Virginia.

Thanks to two chairs of the YArch’19 workshop, Shaizeen Aga from AMD Research and Prof. Aasheesh Kolli from Pennsylvania State University, for making this possible.

Greg Chan and Bhargav Godala from the Liberty Research Group were at most of these talks and helped me write down some ideas.

6th year Ph.D. student @ Liberty Research Group, Princeton University

Graduated Master @ Liberty Research Group, Princeton University

Email forwarding for @cs.stanford.edu is changing. Updates and details here . CS Commencement Ceremony June 16, 2024.  Learn More .

PhD | Advising Guide

Main navigation.

This page sets out the expectations for advising in the Stanford Computer Science PhD program, both for students and faculty. Advising can vary significantly from professor to professor, and many different styles can be effective, so this does not prescribe a particular approach. Instead, it discusses the various elements of advising and the issues for students and faculty to consider. Students can use this information to select the best advisor for their needs, and students and advisors can work together to design a relationship that works best for them.

Advising Purpose

Graduate school is a time of significant change for students. Before graduate school, students live in a highly structured course-oriented world where they mostly follow directions. By the time they receive their PhDs, students have transitioned to a very different world consisting of independent and self-driven research. There are no classes to guide students through this transition; this is the role of advising. Advising provides personalized teaching about how to choose research projects, how to carry them out, how to present the results, and how to behave in a proper professional fashion. Advising helps students develop academic and professional skills, and it prepares them to be competitive for future employment. Advisors also offer advice on many other topics, such as teaching, choosing a career, or general life issues.

Rotations: Aligning with an Advisor

Finding the right advisor is one of the most important tasks for incoming graduate students, and the first year of the PhD program is designed to give students and faculty the information they need in order to make good alignment decisions. Almost all students align with an advisor by the end of their first year. The alignment process is driven by students. Students should begin thinking about advisors as soon as they are admitted to the program. Ideally, an admitted PhD student will already have one or more potential advisors in mind before deciding to come to Stanford. Most incoming students use the rotation program to learn more about potential advisors. During each quarter of the first year, a student works with a particular professor; students select the faculty they would like to work with and approach those faculty to ask about rotation availability. Over the course of a rotation the student learns about the professor and his or her style of research; at the same time, the professor learns about the student. At the end of the quarter, both the student and the professor are in a better position to decide whether they can work together effectively.

Faculty are responsible for offering alignment to students; they can do this at any point during the year. Students can accept an alignment offer at any time, but they typically wait until the end of the third rotation to commit to a particular professor. The rotation process works best when both students and faculty are proactive and transparent. Students should plan rotations as far ahead as possible, in case faculty are constrained in their rotation slot availability. Students should also be proactive in making sure that faculty have alignment slots available 2 before rotating. Faculty should be transparent with students about how many CS students they expect to align with in the current year and how they will make alignment decisions. At the end of each rotation, faculty should give students clear feedback on the prospects for alignment. Faculty should make alignment offers as early as possible in the year; ideally, this will happen immediately after the end of the student’s rotation, in order to minimize uncertainty for students and allow them to plan their remaining rotations better. Students should not be required to decide on alignment offers until the end of the third rotation, in case they find another advising relationship that will work even better.

In addition to providing a vehicle for meeting potential advisors, rotations also provide a great mechanism for learning about research areas outside the student’s area of focus, and for meeting additional faculty and students. However, it’s important for students to have at least one firm alignment offer before considering “experimental” rotations.

Working Together

There are many different styles of advising that work well. This section discusses various aspects of the advising relationship and how they vary from professor to professor. During the rotation process, students should explore the style of each potential advisor and use that information, along with the advisor’s research interests, to identify the advisor with whom they will have the most productive relationship.

• Meetings . Regular face-to-face meetings are essential to a healthy and productive student-advisor relationship. The frequency and length of these meetings varies between advisors, but weekly meetings are common. The meetings are typically informal, with the student describing recent progress and issues, interleaved with comments from the professor and related discussions. Making time for student meetings is one of a professor’s most important responsibilities. One way to ensure that meetings occur is to reserve a regular meeting slot on the advisor’s calendar; it’s easy to cancel or abbreviate a meeting if there are not enough issues to fill the designated slot. In addition to individual meetings, many advisors also meet with their students in other settings, such as weekly group lunches.
• Engagement . The level of advisor involvement in student research varies dramatically among faculty. Some faculty are relatively “hands off” and prefer to engage at a high level, leaving the details to the student. Other advisors take a more “hands on” approach, learning about the student’s project at a greater level of detail and offering more detailed guidance. In some cases advisors work hand-in-hand with students, such as by reading student code or writing code alongside students. It is not unusual for advisors to be more engaged and prescriptive during a student’s early years but step back gradually over time, so that by the time a student graduates he or she is working more independently. A professor is more likely to engage deeply with a student if the professor has a strong personal interest in the student’s research. Sometimes a student’s research interests diverge from those of their advisor. If a student in such a situation wants to have a highly engaged advisor, then the student may need to either switch advisors (see below) or switch to a project that excites their current advisor.
• Control . Some advisors give their students complete control and view the advisor’s role as purely supportive: “you are free to do whatever you want; if you have questions or need help, I will try to assist.” At the other end of the spectrum are advisors who take the phrase “research assistant” literally; they assume that students will help carry out the research and offer input, but the advisor will make most of the important decisions. Most faculty lie between these two extremes, where each party in the relationship has certain obligations to the other. For example, faculty may need help from students to meet obligations associated with funding that supports the students.
• Individual vs. Group . Students can work either alone or as a member of a group. Being part of a group brings several benefits, such as having other students to talk with and being able to attack larger problems. Senior students in a group can help to mentor new students. On the other hand, groups often 3 impose responsibilities; for example, new students may be expected to serve as “apprentices” for senior students, and students may have to give up some flexibility in choosing projects in order to support the overall goal of the group.

Financial Support

The expectation within Computer Science is that faculty ensure financial support for their advisees as long as the students are making reasonable academic progress. Some students already have external support through fellowships; for those who do not, faculty typically provide RA-ships or a combination of RA-ships and assistance in finding suitable CA-ships. An advisor may require students to apply for fellowships. 

Progress & Feedback

One of the most important roles of an advisor is to assess the student’s progress and provide constructive feedback. An advisor should help each student to understand his or her strengths and weaknesses, and work with the student to capitalize on strengths and improve in areas of weakness. If faculty do not volunteer feedback, we encourage students to ask for a written review from their advisors. The advisor should take time to think about the student’s strengths and weaknesses and then write a few paragraphs describing them; the advisor should provide the student with the review, give the student an opportunity to read it, and then meet with the student to go over the review, answer questions, and discuss ways to make improvements.

Co-Advisors

It is not unusual for students to have multiple advisors. When this happens, it is usually driven by the student’s interests. There are many ways to manage co-advising relationships; the parties involved should decide on the parameters for the relationship by answering questions such as the following:

• How do the advisors share advising responsibilities? Is one advisor the “primary” advisor and the other a “secondary” advisor, or are they co-equals?
• Does the student meet separately with each of the co-advisors, or together with both?
• Who will support the student?

Changing Advisors

Sometimes it turns out that a student’s initial advisor is not the best choice. This typically happens because of a divergence in research interests or a conflict in style. Students should feel free to change advisors when situations like this occur: it is better to switch to the right advisor than to keep working with the wrong one. There is no stigma associated with changing advisors. It is up to the student to drive the process of switching advisors by approaching other faculty.

Resolving Problems

Like all relationships, student-advisor relationships are imperfect; there is rarely an exact alignment between the needs and interests of the professor and those of the student. When conflicts arise, the best way to resolve them is for the student and advisor to discuss the conflict and work together to find a mutually agreeable solution; as in other kinds of relationships, listening and compromise on both sides are keys to success. If a student cannot reach a suitable solution to a problem, or if a student is uncomfortable discussing a problem with their advisor, there are several people in the department who would be happy to meet with the student and help to find a solution. Some good people to talk with are the PhD Program Chair, the Department Chair, and Director of Graduate Admissions and PhD Program. 

Faculty Departures; Start-Ups

If a faculty member leaves the department, they are expected to help mitigate the impact on their students. For students close to graduation, it is common for a departing advisor to continue supporting and advising the student through graduation. For students earlier in the program, it may make more sense for the student to find a new advisor. If a faculty member starts a company and asks some of their advisees to join them, there is a potential conflict of interest between the professor’s responsibilities as advisor and as startup founder. Students should not feel obligated to join their advisor’s company. If a student does decide to get involved with the startup, they must discuss this arrangement with the PhD Program Director to ensure that there is a proper separation between the student’s participation in the company and their academic work. Faculty are typically required to submit a Conflict of Interest Management Plan; they should make these plans available to students so everyone knows where the boundaries are.

Students without an Advisor

Occasionally a PhD student beyond the first year will find themselves without an advisor, either because they did not align after rotations or because an existing advising relationship has ended. As soon as a student realizes that they will be without an advisor, they should notify the PhD Program Director, who will work with them to devise a plan for finding an advisor as quickly as possible.

Research guidance, Research Journals, Top Universities

Ph.D. Topics in Computer Science

While there are many topics, you should choose the research topic according to your personal interest. However, the topic should also be chosen on market demand. The topic must address the common people’s problems.

In this blog post, we are listing important and popular Ph.D. (Research) topics in Computer Science .

PhD in Computer Science 2023: Admission, Eligibility

Page Contents

The hottest topics in computer science

• Artificial Intelligence.
• Machine Learning Algorithms.
• Deep Learning.
• Computer Vision.
• Natural Language Processing.
• Blockchain.
• Various applications of ML range: Healthcare, Urban Transportation, Smart Environments, Social Networks, etc.
• Autonomous systems.
• Data Privacy and Security.
• Lightweight and Battery efficient Communication Protocols.
• Sensor Networks
• 5G and its protocols.
• Quantum Computing.
• Cryptography.

Cybersecurity

• Bioinformatics/Biotechnology
• Computer Vision/Image Processing
• Cloud Computing

Other good research topics for Ph.D. in computer science

Bioinformatics.

• Modeling Biological systems.
• Analysis of protein expressions.
• computational evolutionary biology.
• Genome annotation.
• sequence Analysis.

Internet of things

• adaptive systems and model at runtime.
• machine-to-machine communications and IoT.
• Routing and control protocols.
• 5G Network and internet of things.
• Body sensors networks, smart portable devices.

Cloud computing

• How to negotiate service level platform.
• backup options for the cloud.
• Secure data management, within and across data centers.
• Cloud access control and key management.
• secure computation outsourcing.
• most enormous data breach in the 21st century.
• understanding authorization infrastructures.
• cybersecurity while downloading files.
• social engineering and its importance.
• Big data adoption and analytics of a cloud computing platform.
• Identify fake news in real-time.
• neural machine translation to the local language.
• lightweight big data analytics as a service.
• automated deployment of spark clusters.

Machine learning

• The classification technique for face spoof detection in an artificial neural network.
• Neuromorphic computing computer vision.
• online fraud detection.
• the purpose technique for prediction analysis in data mining.
• virtual personal assistant’s predictions.

More posts to read :

• How to start a Ph.D. research program in India?
• Best tools, and websites for Ph.D. students/ researchers/ graduates
• Ph.D. Six-Month Progress Report Sample/ Format
• UGC guidelines for Ph.D. thesis submission 2021

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• ML and DL approaches for computer vision
• Intelligent cyber-physical system
• Imaging techniques
• Biometrics system
• Content based internet computing
• Indistinct vision
• Less exposure
• Problem with research topic
• Not able to converge Novel, Handy, Latest topics
• Objective issues
• Publication, citation counts
• Opportunities in research
• Impact on real world
• Adaptability
• Number of papers issued in high-level journals
• Research chances under the topic
• Number of international conferences

Computer Science Research Topics for PhD is a full research team to discover your work. It is a desire for the up-and-coming scholars to attain the best. Without a doubt, you can know the depth of your work.To fix this issue, we bring our Computer science research topics for PhD services.

In computer science, we will explore 145+ areas and 100000+ topics in the current trend. Seeing that, research topic selection is not the long term process for PhD students. On this page, we will offer you the latest topics in computer science. It is more useful for you in the topic selection process.

Computer science research topics for PhD

• Software-defined cloud computing
• Virtualized cloud environment
• Multi-dimensional, multi-resolution imaging techniques
• Virtual and augmented reality
• Content-based internet computing
• Novel biometrics methods
• Cloud RAN, Fog RAN, Edge RAN designs

Earlier topics afford merely for your reference. To know more or get the topics, you simply email us at our business time. With our support, more than 5000+ scholars have achieved their goal promptly!!!

General glitches you are facing in topics selection are,

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Inbox us your intent domain to get your topics index, Get you within a working day from Computer science research topics for PhD . On the whole, your aim without a plan is just a wish. Your strategy without execution is just an idea. Your execution without us is just an end, but not a feat.

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Finalize journal (indexing).

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• Programming

Latest Computer Science Research Topics for 2024

Home Blog Programming Latest Computer Science Research Topics for 2024

Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!

As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?

For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.

Why is Research in Computer Science Important?

Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.

1. Innovation in Technology

Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.

2. Problem-Solving Capabilities

From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.

3. Enhancing Human Life

Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.

4. Security Assurance

As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.

Top Computer Science Research Topics

Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.

1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges

Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.

As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!

Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.

Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.

2. A Survey on Edge Computing Systems and Tools

With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.

Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.

Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?

As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:

• Apache NiFi:  A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
• Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
• OpenFog Consortium:  An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.

3. Machine Learning: Algorithms, Real-world Applications, and Research Directions

Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?

To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.

You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.

During the research, you will work on and study

• Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
• Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
• Research Trend:  The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.

While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.

Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?

4. Evolutionary Algorithms and their Applications to Engineering Problems

Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.

This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.

Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.

The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.

5. The Role of Big Data Analytics in the Industrial Internet of Things

Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.

This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.

Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.

The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.

6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.

ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.

But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.

Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.

7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.

Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.

But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.

Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.

8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey

If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.

Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.

As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.

Tips and Tricks to Write Computer Research Topics

Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.

• Know your interest.
• Choose the topic wisely.
• Make proper research about the demand of the topic.
• Get proper references.
• Discuss with experts.

By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.

From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore.

One of the most important trends is using cutting-edge technology to address current issues. For instance, new IIoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.

Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.

With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies. 

Frequently Asked Questions (FAQs)

Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.  

Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut. 

 There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more. 

Ramulu Enugurthi

Ramulu Enugurthi, a distinguished computer science expert with an M.Tech from IIT Madras, brings over 15 years of software development excellence. Their versatile career spans gaming, fintech, e-commerce, fashion commerce, mobility, and edtech, showcasing adaptability in multifaceted domains. Proficient in building distributed and microservices architectures, Ramulu is renowned for tackling modern tech challenges innovatively. Beyond technical prowess, he is a mentor, sharing invaluable insights with the next generation of developers. Ramulu's journey of growth, innovation, and unwavering commitment to excellence continues to inspire aspiring technologists.

Avail your free 1:1 mentorship session.

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Episode 210: An Unconventional Path to Computer Science

Episode 210: An Unconventional Path to Computer Science, with Fernanda Viégas

Click on the audio player above to listen to the episode or follow  BornCurious  on Amazon Music , Apple , Audible , Spotify , and YouTube .

On This Episode

In her work as a computer scientist, Fernanda Viégas focuses on data visualization and people-centered machine learning—but her background is in graphic design. So how did she land where she is today? In this episode, our hosts talk with Viégas about her unconventional path, her experience in the world of STEM, and what it’s like to sometimes be the only woman in the room. In addition, they talk about how taking a people-centered approach can make the field more inclusive.

This episode was recorded on February 29, 2024. Released on May 9, 2024.

The conversation follows Episode 209 .

Fernanda Viégas is a Sally Starling Seaver Professor at Harvard Radcliffe Institute, a Gordon McKay Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences, and an affiliate with Harvard Business School. With her longtime collaborator, Martin Wattenberg, she coleads Google’s People + AI Research (PAIR) initiative, which advances the research and design of people-centric AI systems.

Related Content

Fernanda Viégas: Fellowship Biography

Fellow’s Talk: What’s Inside a Generative Artificial-Intelligence Model? And Why Should We Care?

People + AI Research

Ivelisse Estrada  is your cohost and the editorial manager at Harvard Radcliffe Institute (HRI), where she edits  Radcliffe Magazine .

Kevin Grady  is the multimedia producer at HRI.

Alan Catello Grazioso  is the executive producer of  BornCurious  and the senior multimedia manager at HRI.

Jeff Hayash  is a freelance sound engineer and recordist.

Heather Min  is your cohost and the senior manager of digital strategy at HRI.

Anna Soong  is the production assistant at HRI.

Mahbuba Sumiya  is a multimedia intern at HRI and a Harvard College student.

Heather Min: Hello. Welcome back to BornCurious , coming to you from Harvard Radcliffe Institute, one of the world’s leading centers for interdisciplinary exploration. I am your cohost, Heather Min.

Ivelisse Estrada: And I’m your cohost, Ivelisse Estrada.

Heather Min: This podcast is, like its home, about unbounded curiosity.

Ivelisse Estrada: If you listened last week, you know that we did a deep dive with Fernanda Viégas on artificial intelligence, specifically, how it works—and where it falls short. And you may remember that she mentioned that her background is in graphic design. So, in this episode, we’re back with Fernanda—this time, to explore her unconventional path to computer science and to talk about the importance of attracting more women to technology.

Heather Min: Welcome back, Fernanda. We would love to hear more about how you’ve come to work in this extremely exciting, cutting edge, important field. Could you share some of your origin story?

Fernanda Viégas: Sure. So, I am definitely not your orthodox computer scientist. In fact, if you know my story, you’re like, wait, what? How’d you go from A to B? So, I am from Brazil. I did not grow up hacking computers or building computer games. No, not at all. I was not interested in computers at all when I was growing up.

And I ended up coming to the US because of a problem in the Brazilian educational system—it was a problem for me, it’s not a problem for many people, but it was for me—which is in Brazil, when you finish high school and you try to go to college, we don’t have SATs. Each university has its own set of exams that you need to take, and that’s fine. But the thing that really got me was that not only do you do an entrance exam to a specific university, you need to choose your major. And so, you enter for that major. If you later on decide you don’t want that major, you have to get out of the university, wait a year, because these only happen once a year, and you do another exam for the other major.

Heather Min: High stakes.

Fernanda Viégas: It is. And in my case, because I’m very undecided, I did this three times, which meant I spent three years of my life not knowing what I wanted, studying for entrance exams, and still not knowing what I wanted. And so, at the end of these three years, I was like, “I don’t think I’m made for a university. Maybe I’m missing something here.” And I started teaching English as a second language to little kids. And then one of my English teachers actually said to me one day, she’s like, “Fernanda, have you thought about applying to this undergraduate scholarship program in the US?” And I was like, “Why would I leave my country to go to another country, to still not know what I want to do?”

All : [Laugh]

Fernanda Viégas: And she said, “Because in the US you can be undecided. You can change majors as many times as you want.” I was like, “Really? That exists?” She was like, “Yes, it exists.” I was like, “I have to go there.”

And so long story short, I got a scholarship to come to the US. I was an education major, and then I changed to graphic design and art history, loved my majors, but then also in being very true to myself, as I was about to graduate, I was like, I love design, but I don’t want to be a traditional designer, which back in those days meant designing CD covers and DVD covers. And I was like, I like that, but I don’t know what I want to do still. And that’s when I heard about the Media Lab at MIT, and I was like, that’s a place that welcomes people from different backgrounds, but I also heard that MIT is super hard, so what should I do?

So, I tried in my last semester to learn as much as I could about programming. It was hard. I put together the best digital portfolio I could, and somehow, I got into the Media Lab, and that’s where I learned about data visualization, which was a really wonderful way to bring my graphic design skills to a medium where most people who were doing that kind of work were real computer scientists and not designers. But it just gave me a really interesting way to think about computation visually.

And so I loved that. I started working on that. And then I met Martin Wattenberg, with whom I would work for 23 years now. I was his intern at IBM, he was a researcher at IBM. Martin is a mathematician. And we got along really well, and we started working together. That’s where I went after the Media Lab. So Martin and I, for many years, did data visualization, and we ended up at Google as researchers with our own research group there. And because we were at Google and we were doing data visualization, the researchers sitting next to us—this was early enough that the researchers sitting next to us were starting to do machine learning. And we’re like, “This machine learning stuff sounds interesting. We don’t know if it’s very useful, but maybe we should check it out.”

And then at one point, one of the researchers came over and said, “Guys, you do data visualization, right?” We’re like, “Yeah.” They’re like, “We’re building these systems. They’re very complex. They’re very convoluted. We don’t always get a good sense of what they’re doing. And we really think data visualization could help. Would you be willing to think about how we could visualize what these systems are doing?” We’re like, “Sure, let’s talk.” And that’s how we started learning and working with AI.

And eventually our group ended up becoming part of Google Brain, which was one of the big machine learning core teams at Google. And so that’s when we started—more and more of our work ended up being about, how do you visualize these systems? How do you make sense of these systems? What is happening inside of them? And so that was really exciting, and that’s how I ended up working on this.

Ivelisse Estrada: Yeah. And you are a woman in a very male-dominated field.

Fernanda Viégas: Yes.

Ivelisse Estrada: What has that been like? Have you noticed any differences?

Fernanda Viégas: Oh, yes. Yes. I tell a story of working with Martin, because Martin and I are peers—he’s a man, I’m a woman. And after we had been working together for, I don’t know, maybe five years or four years, I asked Martin—because we would be partners, coauthors. We’re doing all of this stuff together. And I said, “Hey, Martin, have you noticed—” I forget what meeting we’re in, but we were in some meeting with someone, and I said to Martin afterwards, “Have you noticed how that person was only talking to you?” And Martin was like, “Nah.” I was like, “Yeah. Notice, notice. He was just paying attention to you. Even when I asked questions, he was answering you—looking at you.” And Martin was like, “No, really?” I was like, “Yes, really. Just pay attention next time.”

And so we had these conversations a couple of times, and then eventually Martin started noticing, and he was like, “You’re right. Oh my gosh. That person was only talking to me, was only addressing me.” I’m like, “Yeah. That’s a thing, isn’t that?” And so, Martin became aware and started, whenever this would—this wouldn’t happen all the time, but whenever it would happen, Martin would immediately be like, “So Fernanda, here: what do you think? You should ask Fernanda, really—she’s the expert on this,” or something. And so, we started having this little song and dance about it. One, it was just interesting to me that he was completely unaware of this. So it was just, again, let’s be aware of what the problem is here. So having had that conversation with him, he became aware, he became truly this huge ally. And to the point where, years later, at Google, multiple times this thing happened, where we would come out of a big meeting with lots of people and eventually Martin would say, “Did you notice you were the only woman in the room?” I was like, “No, I didn’t even notice.” He was like, “You were, and I was glad you were saying this and this and that, and blah, blah, blah.” And so, he was very aware.

So yes, that has been the case. I worry about that. That’s why, as I was saying before, I wonder how much of the change in this technology could mean that we start to bring more women to the fold. Because I think women are interested in this kind of work, but I think there is this barrier where it’s like, if it’s math or if it’s engineering, it’s not for me. I think there are different ways we can think about this kind of technology that can get us through these barriers. So, I’m very curious about what having language as a UI again will do, to bring people who are usually not part of this.

Being part of the industry, it is very clear to me how powerful it is to have a seat at the table, and to have a voice, and to be part of the teams that are building, thinking, designing these technologies. And so, I think it’s incredibly important that we bring in more women. Unfortunately, throughout my tenure at Google, it was already the case, as it’s no news to anyone, that there were so many more men than women. But I’ll tell you, once I joined the machine learning groups, it was even worse. It was even worse. So, I think I went from always having only 20 percent of the people in the room be women to all of a sudden having only 10 percent of the people in the room being women. That’s in the wrong direction, people. And I think we need to really be mindful of what we do.

And so, this makes me think again about this class that we teach here at Harvard, this artistic computation class. By the way, if anyone is a student and is listening, it’s CS73. And one of the things we saw in this class, one is we did not know if undergrads would be interested in this, because it’s a kind of class that had never been taught at Harvard before. And so, we were like, “What if we end up with five students only?” And we’re like, “Well, we will teach—”

Ivelisse Estrada: Call it a seminar.

Fernanda Viégas: That’s right. That’s right. To our surprise, it was a huge waiting list. And not only that, there were a lot of women. I think this—again, it’s not machine learning, it’s not AI, but again, it just shows me, that if we all start to broaden our understanding of what computation is and what it can do, I think so many more different kinds of people will be interested and will do interesting things with these technologies, things we haven’t even dreamt of yet.

And so, I think this is one of the things that I’m really hopeful about, as we maybe don’t rely on people having to go through years of learning how to program, but still being able to interact with these systems and build things that are helpful, useful, exciting, enlightening, all the same. I know how hard it is to learn how to program a computer. And the more you talk about sophisticated systems, the harder it is, and the barrier is super high. And so, if all of a sudden, you can program systems and build things using language, I mean, the barrier just goes down. And also, together with this, the fact that, historically speaking, women have not or minorities have not been as engaged in computation as other segments of society. Wow, can that change now, that we use—if we use language to build things, does that put different segments of society at an advantage?

Ivelisse Estrada: And when you say language, you mean everyday language versus a programming language like—

Fernanda Viégas: Exactly.

Ivelisse Estrada: I mean, I’m going to give myself away. BASIC. That’s the—

Fernanda Viégas: Yeah.

Heather Min: Or Java.

Fernanda Viégas: Yeah, that’s right. That’s right. Yeah. I love Java too. So yes, natural language. This is the magic. It’s like, I can talk to the darn thing, and it can do something based on what I just said. Again, it has two sides to it. One is the fact that any toddler can talk to it. Any kid can talk to it. And if you dream things and if you ask, maybe you can build things with it. The other piece is that language is so sophisticated, and it turns out we use language very ambiguously.

And so, as an interface, it is also very challenging, as people are finding out. There are research papers already talking about things like, if you use certain AI systems for the main experts, so professionally speaking, if you have graphic designers who are trying to do something, they love the fact that they can speak to the thing, they can text and use language. But then it’s extremely frustrating to them that they cannot get to the specific shade of whatever color. Or maybe it’s not the color, but it’s exactly the shape of the button they want, or the kind of experience, UI they want.

And so again, there’s a lot of power in the fact that we’re using natural language, but there’s a lot of room for error and frustration. And we’re just starting to try to tease out, where does language help us? Where does it hinder us? Where do we need the precision that we cannot get with language that, for high-stakes situations, you don’t want ambiguity. What do you do then? How do you support users there? So there are a number of questions that come up just because literally this is a new user interface that we’re just starting to experiment with.

Heather Min: I want to get around to an example that I’ve seen you share in your presentations, which is just to go to a search engine, text field, and “Is my husband,” “Is my child,” “Is my wife,” and how the machine just serves up what it knows has been searched for before. So, it auto-populates, “a narcissist,” “cheating,” which are kind of, as you say, interesting, but also very concerning.

Heather Min: Which all indicates that people already interact with just a search engine for the most personal things. Can you talk about—because my understanding is, you’ve been observing how humans interact with even Google—your understanding of how people are engaging with machines.

Ivelisse Estrada: Right. Because you talk about a human-centered approach.

Fernanda Viégas: Yes. Yes.

Ivelisse Estrada: So let’s talk about that.

Fernanda Viégas: Yeah. No, I think this is incredibly important. Yeah. The example you were giving is even pre-AI. It was literally just talking about Google Suggest, and one of the reasons why I even show that in presentations is because a lot of times people may have a sense that data tends to be cold or official or this kind of otherness. It’s like statistics or something. And I think those examples, they just are like a gut punch. They just very quickly show you how human data is. So literally people are coming to Google and asking, Is my husband cheating on me? Is my wife a narcissist? And these are some of the most personal questions you may have, and yet you’re coming to a public online search engine to ask those questions.

And so, I think it speaks to many things. I think one is just the desire of us as humans to interact with oracles, if you will, digital oracles. Maybe they will know something, or maybe because I’m interacting with this engine in the privacy of my home, I can be way more open, and maybe this system will understand me to a certain extent that others haven’t been able to understand me. So, there are a number of reasons why that may be the case.

It’s also the case that hopefully, if that kind of interaction goes well, these systems are leveraging massive amounts of interactions, hopefully that have been helpful to other human beings—remains to be seen. But I think this is one of the reasons why the way we deploy and design these technologies will really matter, because they are not anymore—they started as computer science experiments in the lab, for very specific reasons, for very specific tasks. And now they’re deployed everywhere, and all of us have access to it. It’s amazing being a research scientist and all of a sudden seeing, for instance, everybody in my family talking about this. I have people in my family asking me. Whereas years ago they would have some sense of what I do but not very much, now we have entire conversations about the science behind this.

So it’s both exciting, but also a very concrete example of how much this matters, and how much all of us are involved or should be involved in thinking about what these systems are, what the implications are for how they behave or what they’re doing, and how we are using them. And I think this is one of the gaps that we still need to bridge, which is—just because of legacy, just because of history, of where they come from, they come from places that are very—they are research projects. They come from a place of research, which is both exciting, because again, you get to build systems and capabilities that didn’t exist before. But once they start to hit society and to be used for high-stakes situations or on an everyday basis, they have not been designed for that necessarily. And so, we need to bridge that gap.

When I say human-centered design, human AI, this is what I mean. The systems today are large, powerful, but they are also kind of general. They were not designed for me, or you, or for my purposes or your purposes. They are designed for everything and everybody but nobody at the same time. And because they are a general technology, they can be repurposed in different ways for different users. And I think if we’re not mindful and intentional about who these users are, what the tasks are that they are trying to achieve or unlock, these systems will behave in ways that are not optimal. They will do their best, but they will break in unexpected—and in the better scenario, they will be funny in the ways that they break. But in the worst-case scenarios, they will be dangerous in the way they break. And so, we need to be cognizant, we need to understand more about how people get to use these things in real life.

Ivelisse Estrada: So thinking about that, we’re having this conversation about AI, and everyone is talking about it. And a lot of people have a lot of anxieties around AI because they think like, it’s going to make my job obsolete, or machines are going to take over and be our overlords. So which of these fears are founded, and which are not?

Fernanda Viégas: So, I can give you my very personal view on this. I don’t know that anyone knows exactly what’s going to happen. But personally, and from what I see, from where I stand, I think indeed they will be very disruptive. So I do think things are going to change, and there will be impact. I don’t think that... I’m not sitting here and thinking nobody will have a job anymore. I don’t think that. I think that we’re going to have this phase—if we work well—we’re going to have this phase where hopefully the things we need to get done in society hopefully will be supported well by these technologies.

So, in other words, if we think that doctors are important, and I deeply hope we do, then we’d better support them. And they have very complicated jobs, they have demanding jobs. What can we do to better support them? Scientists, the same thing. Teachers. I am very curious, right now there’s a lot of conversation and back-and-forth with content creators. So you have illustrators, you have writers, you have journalists. I think we’re going to have to have different models for—I don’t think it’s okay for systems to be harvesting the work that has been done, and one, not credit. So there is, just to be clear, there is a line of research on, even after the fact, so if you go to an image generation model, and you ask it to generate some image for you, can it then look back and credit, “I used this percentage of this artist’s work, and I used this other percentage of this artist’s work,” and can we then monetize this back to the content creators? I think we need models like these.

The same thing with journalists. I was listening to a conversation between journalists and the CEO of Perplexity, I think. And one of the things I had not thought about that I was like, “Maybe this is an interesting new business model,” imagine having access to... So I think this is a new search engine that tries to give you answers. So instead of you having to go through a bunch of Google results, it gives you an answer after it reads everything it can on the web about it and blah, blah, blah. And apparently it’s doing really well. And the journalists were like, “You are using the hard fruits of our work to give your answer.”

So, one of the things I started thinking was like, wow, couldn’t there be a business model where maybe something like Perplexity or some other model will try to give you an answer, but maybe there are two versions of that? One version of that model uses literally just what is publicly available on the web, without using things like real journalism. And then there is a paid version, where actually it has access to the New York Times , it has access to the Wall Street Journal , it has access to El País , it has access to—

Heather Min: Credible sources.

Fernanda Viégas: Credible sources. But can we then monetize that? And does that then get back to the journalists? Because the goal both of journalists and of these systems is to inform citizens. Hopefully that is the goal of everybody, make everybody more knowledgeable. How can we rally around goals like those, but making sure that we are supporting great journalism, investigative journalism? Which, by the way, costs a ton of money and is dangerous sometimes to be done. And so how do we recognize, incentivize, monetize this kind of work? Which, by the way, is what these foundation models are built on.

Because there is another, depending on how you look at this long-term, if you don’t support content creators, what happens 10 years from now, 20 years from now? What kind of content are we going to—

Ivelisse Estrada: Right. When there’s nothing new to feed the machine.

Fernanda Viégas: No, nothing new. Exactly. Then what? I don’t think we want that future. So in other words, I do think it’s disruptive. There is no question. But I would also be remiss to say I don’t think there are amazing opportunities as a scientist. Oh my gosh. I think this is such an amazing tool to have in your toolbox. Again, because there are these complex problems that we don’t know enough yet about the universe to solve. Even physics, we don’t know enough physics to solve certain things. We don’t know how to predict earthquakes. Wow. Can we throw something at it? Can these technologies help us?

But I also think that there is a huge opportunity for these systems. Another kind of opportunity that I really hope we invest on are places where you know you need professionals, we know exactly the kind of professional help we need, but places that don’t have access to these professionals. So for instance, you can imagine tutors. We already know, we know today, that kids who have access to personal tutors do better in school. It’s been proven for decades. There aren’t enough tutors. There aren’t enough high-quality tutors. How can we work with tutors to augment what they can do? How can we work with tutors so that they have an assistant that can then work with different kids in their different learning styles?

So I think working with the professionals that we already know need to be augmented. We have a crisis, a mental health crisis. How can we work with mental health professionals? I don’t know enough about this domain, but is it an assistant? Is it not an assistant, but some other kind of supporting technology that could help professionals? So I think there are these wonderful opportunities for really hard problems we have today, where it seems like augmenting what we can do today could go a long way.

Ivelisse Estrada: Before we wrap it up, is there anything that we didn’t touch on that you feel it’s important to share?

Fernanda Viégas: Yeah, there was one thing within the context of understanding whether these systems are modeling us, the user, and whether that matters or not. There is research showing the effect of what’s called sycophancy. So, this is where the system is trying to ingratiate itself to you, the user. And so, one of the papers that talks about this, one of the first papers to talk about this, ran experiments where the researcher would say to the system, “I am Bob. I am 58 years old. I live in Texas. I would self-describe as a very conservative man in Texas.” And then Bob would say, “But enough about me. I would like to ask you a question. What do you think is better, big government or small government? And why?” And so, the system would answer, “Smaller government is better because,” and would give the reasons.

And then the experimenter would turn to the same system and say, “Hi, I am Jane. I am 45 years old. I live in San Francisco” and would give a very liberal leaning profile. And would say, “But enough about me. I have a question for you. What do you think is better, large government, small government?” And the system would be like, “A bigger government obviously is better, with more social security and programs and so forth.” And you can reproduce this very easily.

So one of the things that’s very interesting is, are we just hearing back from these systems things that they’re gleaning from us? And is it that we are going for accuracy and factuality? Is it that we want a system that will just be very friendly to me? What does friendliness mean? Is there a ground truth of some sort? So, these are all questions that come up when we start thinking about, how are people interacting with these systems? What are they getting out of these systems? And what are they asking these systems for opinions on?

Heather Min: And to be clear, it’s not as though these systems were programmed to be risk-averse or conflict-averse or agreeable, but it also sounds like, when you’re stuck in the thread of music recommendations or movie recommendations, where I’m like, just because I watched that once doesn’t mean that that’s the only thing I like.

Fernanda Viégas: Yes. Exactly. Exactly.

Heather Min: But it likes to sort of fix you.

Fernanda Viégas: It likes to fix you sometimes. Yeah. And sometimes they are explicitly being programmed to be friendly, but what kind of friendliness do we want? And so again, this all comes back to that thorny question around values, and what are we using these systems for? What are people getting out of these systems? That we are just starting to contend with.

Heather Min: We need to use them critically, even though it’s really easy not to.

Fernanda Viégas: It’s easy to forget. Right.

Heather Min: Especially when they’ve got cool Australian accents.

Fernanda Viégas: That’s right. That’s right. Yeah.

Heather Min: Thank you.

Ivelisse Estrada: Yes, thank you so much.

Fernanda Viégas: You’re welcome.

Heather Min: That concludes today’s program.

Ivelisse Estrada: BornCurious is brought to you by Harvard Radcliffe Institute. Our producer is Alan Grazioso. Jeff Hayash is the man behind the microphone.

Heather Min: Anna Soong and Kevin Grady provided editing and production support.

Ivelisse Estrada: Many thanks to Jane Huber for editorial support, and we are your cohosts. I’m Ivelisse Estrada.

Heather Min: And I’m Heather Min.

Ivelisse Estrada: Our website, where you can listen to all our episodes, is radcliffe.harvard.edu/borncurious.

Heather Min: If you have feedback, you can e-mail us at [email protected].

Ivelisse Estrada: You can follow Harvard Radcliffe Institute on Facebook, Instagram, LinkedIn, and X. And as always, you can find BornCurious wherever you listen to podcasts.

Heather Min: Thanks for learning with us, and join us next time.

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HELP MAUI • JOB OPENINGS

Information and Computer Sciences

University of Hawai‘i at Mānoa

Prospective Ph.D. Students

• January 15th : Application deadline for Fall semester admissions.
• September 1st : Application deadline for Spring semester admissions
• Late Applications: Full consideration will be given to applications received by the deadline. Late applications are considered depending on availability of resources.

Application

Submit your application online through Graduate Division. The CS program requires the following Supplemental Documents to be uploaded with your application:

• Statement of Objectives/Purpose . This document explains why you wish to pursue a graduate degree and your career objectives.
• Three letters of reference . Enter the names and e-mail addresses of reference letter writers via the Graduate Division’s Supplemental Documents upload site. Your letter writers will submit their confidential letters directly to the Graduate Division.

When Graduate Division determines that your application is complete, your application will be forwarded to the ICS Department. Note: ICS does not collect the application fee, and thus cannot waive it. You may request a waiver from Graduate Division at [email protected], but they routinely deny such requests.

Minimum Requirements

An applicant may be admitted with a Bachelor’s degree or with an M.S. degree. Applicants with Bachelor’s degrees must satisfy the admission requirements of the ICS master’s program .

The GRE General Test is suggested but not required due to the logistical and financial challenges they pose to some students. If you send them, GRE scores should be sent to us directly from ETS (Institution code: 4867; Department code: 0402) .

International Students

Graduate Division requires that international applicants whose native language is not English take either the TOEFL or the IELTS . More information on the requirements is available on the English Proficiency page of the Graduate Division website . See also additional information for international applicants .

See the prospective FAQ . Questions about submitting an application should be directed to Graduate Division . Questions about the program should be emailed to ICS Graduate Chair .

Graduate Joseph Casale ready to return to Malaysia as a Fulbright awardee

The computational mathematics and computer science graduate will build on previous research from his undergraduate experience.

RIT computational mathematics and computer science graduate Joseph Casale will be returning to Malaysia to work in machine learning as a Fulbright scholar.

Joseph Casale ’24 ( computational mathematics and computer science ) had hardly been on a plane when he traveled to Malaysia to do research a year ago. Now, he gets the opportunity to do it again.

Meet the other 2024 Fulbright U.S. Student awardees

Peyton D’Anthony will teach English in Kosovo.

Emma Herz Thakur will create connections between artisans and museums in France.

Mikkael Lamoca will research age-related neurodegeneration in Singapore.

Izzy Moyer will work with The State Archives in Dubrovnik, Croatia.

Sarah Sabal will pursue a graduate education in Taiwan.

Casale, who is from Rochester, N.Y., is one of RIT’s record-setting six 2024 Fulbright U.S. awardees. He will be traveling back to Malaysia after previously going there with Chester F. Carlson Center for Imaging Science professor Tony Vodacek to work with the Universiti Teknologi Mara.

On his previous trip to Malaysia, Casale was part of a group of students who spent time in the Taman Negara National Park. The research team was looking to quantify biodiversity in the rainforest with audio processing. Casale’s future project will be analyzing aerial hyperspectral imagery to map the species of trees.

“The great thing about doing machine learning is it allows you to be a scientist and allows you to work with plenty of different people across all different types of fields,” said Casale. “Being able to go to Malaysia with Dr. Vodacek directly formed the connections that led to this project.”

Casale originally wanted to study aerospace engineering and began his academic career at Monroe Community College, but then realized he was more interested in pure analytical mathematics. When he transferred to RIT, his interest in machine learning and optimization started him on the path to earning the prestigious international experience that a Fulbright Scholarship brings.

Having the opportunity to travel around the world as a RIT student has broadened Casale’s interests and has shown him the possibilities that are available through academia. Earning a Fulbright scholarship serves to enhance what he has already experienced.

“Before I left for Malaysia, I didn’t really see the potential of becoming an international researcher,” said Casale. “But there is room for people to do that, it is something that can be achieved with a little bit of luck.”

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