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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.

King's College London

Computer science research phd, key information.

The Department of Informatics has an extensive research profile, with major externally funded projects, a strong publication profile and significant research activity.

Our research is organised around our research groups, and you can find details of the range of current research projects and interests on the Department's research pages .

If you are interested in joining us to undertake PhD research, you should identify topics and academic staff in your area of interest. If you cannot find your chosen topic or area on our individual research section or subgroup pages, contact a relevant member of academic staff for further information and then follow the application procedure.

Current number of academic staff: 79

Current number of research staff: 37

Head of department: Professor Luc Moreau

Course intake: Approximately 25-30 per year

Research income

Currently, the Department attracts approximately £4m in research funding annually.

Recent publications

All academics in the Department publish regularly, with well over 100 publications per year.

Partner organisations

We have strong links with industry, government and other academic institutions. Our research has been supported by several companies from the aerospace, automotive, financial, IT and telecommunications sectors.

Recent events

We host several workshops and conferences and other regular research meetings. Please check our website for forthcoming events.

• How to apply
• Fees or Funding

For funding opportunities please explore these pages:

• List of funding opportunities
• External funding opportunities for International students
• King’s-China Scholarship Council PhD Scholarship programme (K-CSC)

UK Tuition Fees 2023/24

Full time tuition fees: £6,540 per year

Part time tuition fees: £3,270 per year

International Tuition Fees 2023/24

Full time tuition fees: £28,260 per year

Part time tuition fees: £14,130 per year

UK Tuition Fees 2024/25

Full time tuition fees: £6,936 per year

Part time tuition fees: £3,468 per year

International Tuition Fees 2024/25

Full time tuition fees: £30,240 per year

Part time tuition fees: £15,120 per year

These tuition fees may be subject to additional increases in subsequent years of study, in line with King's terms and conditions.

• Study environment

We are a department with many internationally recognised researchers and visiting academics, large groups of PhD students, research assistants, national and international projects, collaborations with other departments as well as links with industry. We offer an exciting environment and excellent opportunities for research.

Our PhD students have access to good library facilities, designated PhD offices within the Department where PhD students can dock an assigned laptop for use throughout their studies, Regular group seminars are organised providing PhD students chance to showcase their research and receive feedback from academic staff and peers, and college-based training in transferable and research skills.

The Department is located on the Strand Campus, in the heart of central London, close to the cultural activities of the West End and the South Bank, to the major departments of state at Whitehall, and to the leading financial institutions of the City, and within easy reach of major transport links. Our facilities are within easy reach of the British Computer Society and the Institute of Engineering & Technology (and the IET Library), with access to a formidable collection of scientific journals and other technical material.

The Department moved to the historic Bush House in the summer of 2017, featuring state-of-the-art teaching and office spaces. Although the Department is fairly large in size, there is a friendly and inclusive culture, with regular social and celebratory events to bring staff and students together. Our staff and students come from all over the world, which provides a rich environment for teaching and research. Diversity is positively encouraged - find out more about the work we’re doing to ensure an inclusive and supportive working environment.

The scope of our research is defined by the interests of our research groups.

Postgraduate training

Faculty and College induction courses are scheduled at the beginning of your degree to prepare you for life as a PhD student. All students are required to complete 10 days of training each year. There is a centrally provided programme of related and transferable skills training coordinated by the Centre for Doctoral Studies .

Research students are also encouraged to submit papers to conferences, and we try to provide financial support for them to travel to present their papers.

Our research students are also encouraged to teach alongside their studies to help prepare them for a potential future career in academia.

• Entry requirements
• Research groups

Algorithms and Data Analysis

The group develops algorithmic solutions and concrete implementations for various applications.

Cybersecurity

The group studies design, modelling, analysis, verification and testing of networks and systems.

Distributed Artificial Intelligence

The group explores the use of AI in social and economic contexts where an intelligent entity may be interacting with other entities.

Human Centred Computing Research

The group is concerned with the design, development and evaluation of human computer systems.

Reasoning and Planning

The group focuses on the fundamental AI challenge of creating, representing and reasoning.

Software Systems

The group studies design, modelling and engineering of software systems.

Centre for Doctoral Studies

NMES Graduate School

A supportive and engaging environment for PhD students

Funding & Scholarships for PhD students

The Centre for Doctoral Studies helps secure funding for students...

NMES Graduate School: Virtual Open Event Session One

The NMES Graduate School Virtual Open Events for prospective postgraduate...

NMES Graduate School: Virtual Open Event Session Two

PhD in Computer Science

The PhD in Computer Science program provides students with the advanced coursework and groundbreaking research opportunities they need to contribute at the forefront of the world’s fastest-growing fields. Forging knowledge in 15 core areas like artificial intelligence, data science, programming languages, and human-centered computing, you’ll gain significant expertise in conducting and presenting the results of your research. Ultimately, you’ll produce and defend original work that contributes to critical discourse in your chosen area.

• Explore plan of study
• View program requirements
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Khoury College doctorate students gain deep knowledge and invaluable experience—preparing you for a research career in academia or industry.

Khoury Computer Science PhD graduates have found prestigious positions across industry and academia.

Tenure-track faculty:

• University of Michigan, Ann Arbor
• University of British Columbia (UBC)
• Indiana University
• University of Maryland
• University College London
• NC State University
• UMass Boston
• City University of Hong Kong

Postdoc research scientists:

• University of Paris
• Virginia Tech
• Microsoft Research
• GE Global Research

Senior software engineers and industry leaders:

Students graduating with a PhD in Computer Science will:

• Gain a broad understanding of computer science fundamentals, spanning a substantial portion of the following core areas: artificial intelligence and data science, human-centered computing, software, systems, and theory
• Gain significant expertise in at least one research area in computer science
• Produce and defend original research in an area of computer science
• Be able to communicate research results effectively in both oral and written forms

Our flagship campus in Boston is just minutes away from esteemed universities, exciting start-ups, and leaders in tech, finance, health care, and more.

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Artificial Intelligence, Machine Learning, Privacy/FATE 

Theory and Computation

Systems, Databases, Software Engineering, Cyber-Physical Systems, Security

Computer Vision, Robotics, Graphics, HCI

Artificial intelligence, machine learning, privacy/fate .

Researchers in artificial intelligence (AI) seek to understand and develop machines with human-level intelligence by exploring the academic and real-world challenges surrounding AI. 

At USC’s Department of Computer Science, we are pioneering breakthroughs in a full spectrum of topics related to AI, including machine learning, computer vision and image processing, human-robot interaction, speech and language analysis, information extraction and privacy-protection.

Our researchers are working in areas where artificial intelligence has been under study for decades—like language—and where the tools are just starting to make inroads—such as efforts to combat human trafficking, diagnose fetal alcohol syndrome, and prevent terrorist attacks using limited resources. 

We understand that the long-term goal of building intelligent machines relies on collaboration across many fields. That’s why we also work closely with researchers across application domains, such as health care, social work and linguistics.

Affective Computing Group Automatic Coordination of Teams (ACT) Lab Center for Autonomy and AI Center on Knowledge Graphs Cognitive Architecture Cognitive Learning for Vision and Robotics Lab Collaboratory for Algorithmic Techniques and Artifical Intelligence (CATAI) Computational Linguistics Computational Neuroscience Lab (iLab) Computational Social Science Laboratory IRIS Computer Vision Lab (CV-Lab) Data, Interpretability, Language and Learning (DILL) Lab Data Science Lab Database Lab (Dblab) Haptics Robotics and Virtual   ICT Natural Language Dialogue Group IDM Artificial Intelligence Laboratory Information Laboratory (InfoLab) Intelligence and Knowledge Discovery (INK) Research Lab Integrated Media Systems Center (IMSC) Interaction Lab Interactive and Collaborative Autonomous Robotic Systems (ICAROS) Lab Interactive Knowledge Capture Machine Learning and Data Mining Lab (Melody-Lab) Polymorphic Robotics Lab Privacy Research Lab Robotics and Autonomous Systems Center (RASC) Robotic Embedded Systems Lab Robotics Research Lab Semantic Information Research Speech Analysis and Interpretation Lab (SAIL) USC Brain project USC Center for Artificial Intelligence in Society USC Center for Autonomy and Artificial Intelligence 

Aleksandra Korolova Andrew Gordon Aram Galstyan Barath Raghavan Bill Swartout Bistra Dilkina Craig Knoblock Cyrus Shahabi David Kempe David Pynadath David Traum Fred Morstatter Gale Lucas Gaurav Sukhatme Greg Ver Steeg Haipeng Luo Heather Culbertson Jay Pujara Jesse Thomason Jiapeng Zhang John Heidemann Jonathan Gratch Jonathan May Jose Luis Ambite Jyotirmoy Deshmukh Kallirroi Georgila Kristina Lerman Laurent Itti Leana Golubchik Maja Matarić Michael Zyda Mohammad Soleymani Muhammad Naveed Mukund Raghothaman Ning Wang Paul Rosenbloom (Emeritus) Pedro Szekely Ram Nevatia Robin Jia Satish Thittamaranahalli Saty Raghavachary Shaddin Dughmi Shang-Hua Teng Srivatsan Ravi Stefanos Nikolaidis Sven Koenig Swabha Swayamdipta Tatyana Ryutov Ulrich Neumann Victor Adamchik Weihang Wang Wei-Min Shen Wensheng Wu Xiang Ren Yan Liu Yolanda Gil

USC has a strong and active background in modern theoretical computer science,  with research spanning a broad range of topics.   Areas of particular interest include the theory of algorithms and optimization, graph theory, scalable algorithms, theory of machine  learning, computational geometry, complex analysis, computational complexity, algorithmic number theory and cryptography.

Our researchers in this area are particularly motivated by bridging the gap between theory and practice, such as non-blockchain digital currencies, social network analysis, smoothed analysis, bilingual learning and post-quantum cryptography.

In addition, we have many strong connections to other fields, including economics and game theory, pure mathematics, applied mathematics and scientific computing, network science, sociology, as well as evolution of concepts, ideas and organisms.

Collaboratory for Advanced Computing and Simulations (CACS) Collaboratory for Algorithmic Techniques and Artifical Intelligence (CATAI) CS Theory Group

Aaron Cote Aiichiro Nakano Aleksandra Korolova Bistra Dilkina David Kempe Haipeng Luo Jeffrey Miller Jiapeng Zhang Jonathan May Len Adleman Ming-Deh A. Huang Satish Thittamaranahalli Shaddin Dughmi Shang-Hua Teng Shawn Shamsian Srivatsan Ravi Victor Adamchik

The demands on modern computing systems are increasingly complex, from small embedded systems in phones, laptops and wearables, to large-scale cloud computing and high-performance networks.

Systems, databases and software engineering research at USC aims to develop innovative hardware and software across the computing spectrum for existing technologies and to support future power-efficient, sustainable and secure computer systems.

From smart cities and intelligent transportation systems to personalized medicine, next-generation computer systems will require new, innovative and visionary approaches to hardware, wired and wireless communication.

At USC, researchers investigate various issues in the design and analysis of infrastructures for large networks. We focus on fundamental aspects of information acquisition, processing, security, privacy, storage, and communication.

Our research interests include crowd-sensing, program analysis, privacy-preserving systems, network design and management, software-defined networking, cloud computing, internet measurement, software verification and synthesis, advanced 5G wireless networks and data center design.

ANT (The Analysis of Network Traffic Lab) Autonomous Networks Research Group Center for Computer Systems Security Center for Systems and Software Engineering (CSSE) Center on Knowledge Graphs Collaboratory for Advanced Computing and Simulations (CACS) FPGA/Parallel Computing Group Information Laboratory / USC Integrated Media Systems Center (IMSC) Networked Systems Lab Quantitative Evaluation & Design Lab (QED ) Safe Autonomy and Intelligent Distributed Systems (SAIDS) Lab STEEL Security Research Lab The Postel Center USC Database Lab

Andrew Goodney Barath Raghavan Bill Cheng Bill Swartout Chao Wang Claire Bono Clifford Neuman Craig Knoblock Cyrus Shahabi Ellis Horowitz Ewa Deelman Fred Morstatter Jelena Mirkovic Jeffrey Miller John Heidemann Jose Luis Ambite Jyotirmoy Deshmukh Lars Lindemann Leana Golubchik Mark Redekopp Mukund Raghothaman Nenad Medvidovic Pedro Szekely Ramesh Govindan Saty Raghavachary Shahram Ghandeharizadeh Shawn Shamsian Srivatsan Ravi Tatyana Ryutov Weihang Wang Wensheng Wu William G.J. Halfond Xiang Ren

Computer Vision, Robotics, Graphics and HCI

At USC, the areas of computer vision, robotics and graphics represent the interface between computers and the rest of the world. 

Robotics at USC focuses on developing effective, robust, human-centric, and scalable robotic systems. In this area, our expertise ranges from socially assistive robotic and novel haptics technology for virtual touch to complex human-robot interaction and  multi-robot systems.

In computer vision and graphics, our researchers bridge physical and digital worlds with powerful recognition and analysis algorithms, as well as immersive technologies, such as augmented and virtual reality.

In computer vision, our strengths include object detection and recognition, face identification, activity recognition, video retrieval and integrating computer vision with natural language queries.

Our graphics researchers focus on interactive techniques and the simulation and synthesis of multimedia, 3D content and virtual worlds, including image-based modeling and reconstruction, shape analysis, 3D face processing, human digitization, efficient physics simulation, image and video-based rendering techniques.

Last but not least, USC Games, a collaboration between the Department of Computer Science and the School of Cinematics Arts, is recognized as one of North America’s top game design programs, according to Princeton Review.

Cognitive Learning for Vision and Robotics Lab Computer Graphics Group Computer Graphics, Animation and Simulation Laboratory Computer Graphics and Immersive Technologies (CGIT) Geometry and Graphics Group Haptics Robotics and Virtual Interaction (HaRVI) Lab Autonomous Robotic Systems (ICAROS) Lab Interaction Lab Polymorphic Robotics Lab Robotics and Autonomous Systems Center (RASC) Robotic Embedded Systems Lab Safe Autonomy and Intelligent Distributed Systems (SAIDS) Lab USC Geometric Capture Group Vision & Graphics Lab at USC ICT

Andrew Goodney David Pynadath David Traum Gale Lucas Gaurav Sukhatme Heather Culbertson Jernej Barbic Jesse Thomason Jonathan Gratch Kallirroi Georgila Lars Lindemann Laurent Itti Maja Mataric Mark Redekopp Michael Zyda Mohammad Soleymani Ning Wang Oded Stein Paul Rosenbloom Ram Nevatia Satish Thittamaranahalli Saty Raghavachary Stefanos Nikolaidis Sven Koenig Ulrich Neumann Wein-Min Shen Yolanda Gil

Published on August 2nd, 2019

Last updated on November 17th, 2023

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College of Computing

Ph.D. in Computer Science

All students in the program receive the same degree regardless of their interest area, specialization, research focus or school affiliation. New students are affiliated with the school in which their advisor resides, but none of the schools impose any special requirements compared to another.

As a research-oriented degree, the Ph.D. in Computer Science prepares exceptional students for careers at the cutting edge of academia, industry and government. Students are expected to demonstrate excellence in both defining and executing a substantial research project that forms a novel contribution to the state of the art in computing. With a highly individualized program of study, the degree provides students with depth in their chosen research area coupled with a rigorous breadth of knowledge across the discipline.

First granting the doctoral degree in 1969 (as a Ph.D. in Information and Computer Science), the College of Computing represents a continuation of one of the earliest and most well-established graduate Computing institutions in the United States, and in the world. This stature is reflected in our national and international rankings and, most importantly, in the quality of the students who have graduated from our program.

The CS Ph.D. Structure

The coursework component of the Computer Science Ph.D. consists of an introductory course on graduate studies (CS 7001), along with the separate breadth and minor requirements.

The breadth requirement is intended to give students a broad competency across the discipline of computing through coursework in a range of the College's different research areas. This requirement is satisfied by taking five classes from across the College's different research areas and must include a Programming Proficiency course and a Theory course.

The minor is a 9-hour sequence of courses from outside the College that constitutes a coherent program of study and is determined by the student and advisor. The minor builds non-Computing expertise in an area related to the student's core research area.

As students progress, they must select a primary area of research and pass a qualifier (comprehensive exam) in that area to demonstrate mastery of the field in their chosen area, and readiness to do research.

While coursework plays an important role in the Ph.D., by far the most important component of the degree is the student's individual dissertation research project. This project should contribute to new knowledge in the field of computing, and should demonstrate the student's proficiency in defining and executing a compelling research agenda.

The dissertation research plan is formalized in a written proposal followed by an oral presentation. When a student passes his or her proposal, the student is admitted to candidacy and proceeds with the dissertation research, which is completed with the successful defense and submission of the approved doctoral dissertation.

Program of Study

Want to know more details about the program of study for the Ph.D. in CS? Find out about breadth component areas and courses, minors, qualifying exams, and more on the Ph.D. CS - Program of Study page.

Explore the Program of Study for the Ph.D. in CS

Computer Science Research

The breadth of the College's research endeavors makes our doctorate degree in computer science unique: the research specializations in the College span what is typically found in a "traditional" CS department, along with elements found in EECS, robotics, or information schools in other universities. This diversity allows students to formulate a unique individual program of study all within the CS degree, which may be impossible at other universities.

Learn more about our Areas of Research

Admissions Requirements and Applications

If this kind of work interests you and fits with your career aspirations, why not go ahead and apply?

Need more information about our admissions requirements? Find an overview of these requirements on our Ph.D. CS - Admissions Requirements page.

Ph.D. CS - Admissions Requirements

Current Ph.D. in CS Student Information

If you are a current student in our Ph.D. in Computer Science program, you can find information on your qualifying exams on our website. If you still cannot find the information you are looking for, please reach out to your assigned program advisor. 

Ph.D. CS - Qualifier Exam Information

CS Ph.D. Student Handbook

Older handbook:

2022  CS Ph.D. Student Handbook  

2021 CS Ph.D. Student Handbook

From the Catalog:

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

Research Areas

Research Areas

Autonomous and cyber-physical systems.

Subareas: Real-time and Embedded Systems, Sensor Systems, Mobile Computing, Control Theory and Systems, Formal Methods, Automated Verification and Certification Faculty:  Alterovitz ,  Anderson , Chakraborty , Duggirala ,  Nirjon

More on Autonomous and Cyber-Physical Systems

Bioinformatics and computational biology.

Subareas: Computational Genetics, Computational Immunology, Proteomics, Statistical Genetics, Single-Cell Bioinformatics Faculty: Ahalt ,  Krishnamurthy , Marron , McMillan , Snoeyink , Stanley

More on Bioinformatics and Computational Biology

Computational Immunology: Advancements in high-throughput flow and mass cytometry technologies have enabled the ability to study the immune system at an unparalleled depth.  Understanding immunological adaptations to particular diseases and in aging and development offers unique opportunities to develop novel diagnostic tests or to propose specialized treatments or lifestyle interventions to optimize human health. Using single-cell flow and mass cytometry data collected across multiple individuals, our goal is to develop new computational techniques to identify and link heterogeneity in the cellular landscape to external variables of interest, such as, a clinical phenotype or diagnosis. Recent advances in imaging cytometry also enable taking images of tissues and studying the spatial organization of immune cells. Application areas of interest include pregnancy, HIV, neuroimmunology, and T-cell biology. Relevant People Natalie Stanley ; Collaborating Departments: Microbiology and Immunology , Computational Medicine Program , Department of Anesthesia

Development and Differentiation, and Metagenomics: We use novel measurement techniques as well as machine learning methods in understanding the interplay between these areas, with the aim of discovering the forces that shape the immune system throughout life. The overarching goal is to apply the insights from such analyses to propose new treatments for cancers.

Single-Cell Bioinformatics: Cellular heterogeneity, or the synergy of diverse and specialized cell-types drive a range of biological phenomena. Several technologies exist for measuring various properties (e.g. gene expression, protein expression) in individual cells, which allows for their comprehensive characterization and analysis in clinical or biological applications. Single-cell measurements can be studied in vitro to understand the etiology of disease.  For example, in hypoxia of heart muscle cells, the  cells become scar tissue and lose their muscle function.  This process can be studied by looking at single cell transcriptomes to determine the order of events. Further, it can be possible to “reprogram” this sequence of events to avoid the adverse outcome. See some of our recent work in reprogramming scar tissue cells to recover some heat muscle cell functionality.

Technologies and Data Science Problems:   Single-cell datasets produced with technologies, such as single-cell RNA sequencing (scRNA-seq) or flow and mass cytometry reveal a unique data structure where there are several high-dimensional single-cell measurements per profiled sample, which need to be efficiently integrated. 

Flow and Mass Cytometry : Flow and mass cytometry are high-throughput single-cell proteomics technologies for systematic analysis of the immune system. Often applied for the analysis of human blood and tissue samples, the produced datasets can collectively contain millions of cells. We focus on developing new computational techniques for representing, dissecting, and mining this large volume of cells to identify immunological adaptations in disease and development. 

Relevant People: Leonard McMillan , Natalie Stanley

Computer Architecture

Subareas: Accelerators, Clockless Logic, Energy-efficient Computing, Security Faculty: Porter , Singh , Sturton

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Energy-Efficient Systems: With the explosive growth in mobile devices, there has been a push towards increasing energy efficiency of computation for longer battery life. Reducing power consumption is also important for desktop computing to alleviate challenges of heat removal and power delivery. A special focus in our department has been on the development of energy-efficient graphics hardware. Another area of future interest is energy-harvesting systems, which are ultra-low-power systems that operate on energy scavenged from the environment.

Asynchronous or Clockless Computing: Asynchronous VLSI design is poised to play a key role in the design of the next generation of microelectronic chips. By dispensing with global clocks and instead using flexible handshaking between components, asynchronous design offers the benefits of lower power consumption, greater ease of integration of multiple cores, and greater robustness to manufacturing and runtime variation. Our researchers work on all aspects of asynchronous design, including circuits, architectures, and CAD tools. A key area of interest is application to network-on-a-chip for integration of multiple heterogeneous cores.

Computer Graphics

Subareas: Animation & Simulation, Graphics Hardware, Modeling, Rendering, Tracking, Virtual Environments, Visualization Faculty: Alterovitz , Chakravarthula , Fuchs , Marks , Sengupta , Singh , Snoeyink , Daniel Szafir , Danielle Szafir

More on Computer Graphics

Computer vision.

Subareas: Geometric Vision, Language & Vision, Recognition Faculty: Ahalt , Bansal , Bertasius , Marks , Niethammer , Sengupta

More on Computer Vision

Human-computer interaction.

Subareas: Assistive Technology, Haptics, Human Factors Analysis, Sound & Audio Display, User-Interface Toolkits, Virtual Environments Faculty: Dewan , Marks , Nirjon , Porter , Pozefsky , Srivastava , Stotts , Daniel Szafir , Danielle Szafir

More on Human-Computer Interaction

Wearable devices, such as smart watches and smart glasses, and other common sensors are increasingly facilitating new modes of interaction with modern computers—making the goal of ubiquitous computing realizable. A major research direction in HCI at UNC is exploring design techniques and system support to more easily extend desktop and phone applications onto devices with widely varying form factors and interaction modes.

Another significant research direction at UNC is exploring assistive technologies for users with impairments, such as learning disabilities, blindness, and low vision. These populations face significant barriers to education and employment that we aim to reduce, as well as study different modes of interaction with computers.

Machine Learning and Data Science

Subareas: Data Integration, Internet of Things, Knowledge Discovery, Machine Learning, Scientific Data Management, Visual Analytics Faculty: Ahalt , Bansal , Bertasius , Chaturvedi , Krishnamurthy , Marks , McMillan , Niethammer , Nirjon , Oliva , Sengupta , Srivastava , Danielle Szafir , Yao

More on Machine Learning and Data Science

Machine Learning: The problems we study combine vast amounts and disparate types of measurements with equally complex prior knowledge, posing unique challenges for machine learning. Our interests include both modeling paradigms, such as Bayesian nonparametric methods, and inference methodologies, such as MCMC, variational methods and convex optimization.  We also work on structured, interpretable, and generalizable deep learning models. Other topics of focus include multi-task learning, reinforcement learning, and transfer learning.

Medical Image Analysis

Subareas: Biomechanical Modeling, Diffusion Imaging, Image-guided Interventions, Segmentation, Shape Analysis, Registration Faculty: Alterovitz , Marron , Niethammer , Oguz , Pizer , Styner

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Natural language processing.

Subareas: Language Generation, Multimodal and Grounded NLP (with Vision and Robotics), Question Answering and Dialogue Faculty:  Bansal , Chaturvedi , Srivastava

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Subareas: Distributed Systems, Internet Measurements, Multimedia Systems, Multimedia Transport, Network Protocols Faculty: Aikat , Dewan , Jeffay , Kaur , Mayer-Patel , Nirjon , Pozefsky

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Operating systems.

Subareas: File Systems, Virtualization, Concurrency, Software Support for Secure Hardware Faculty: Anderson , B. Berg , Jeffay , Porter

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This area has substantial overlap with a number of other research areas, including cyber-physical systems, real-time systems, mobile systems, networking, architecture, human-computer interaction, and security.

Real-Time Systems

Faculty: Anderson , Jeffay , Nirjon

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Subareas: Assistive Robotics, Manipulation, Medical Robotics, Motion Planning & Control, Robot Learning, Robot Perception (see: Computer Vision) Faculty: Alterovitz , Bansal , Snoeyink , Daniel Szafir

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Subareas: Cloud Computing Security, Cryptography, Hardware Security, Mobile Device Security, Network Security Faculty: Aikat , Eskandarian , Kwong , Porter , Sturton

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Network security: Today’s Internet infrastructure is a common target of attack and the vehicle for numerous unwanted activities in network applications (e.g., spam, phishing).  We are conducting research to evaluate the extent of these vulnerabilities and to develop defenses against them. This includes research on both protecting the Internet infrastructure from attack and designing defenses within the context of network applications.

Cloud computing security: The use of cloud servers to outsource data and processing has become increasingly common. Because cloud facilities are shared, however, a customer’s data and processing may reside with those of competitors or attackers, and so privacy and integrity of the customer’s activities are paramount. We are developing technologies to better protect data and processing in such threatening environments.

Subareas: Algorithms, Automated Theorem Proving, Formal Methods Faculty: Anderson , B. Berg , Duggirala , Eskandarian , Snoeyink , Sturton

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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 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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In the past five years, Computer Science faculty have had research collaborations with every other college at Purdue. The work of the computer scientist is applicable just about everywhere. Though research activity spans many broad areas, the list below reflects the interests and expertise of the faculty summarized in 14 areas.

Artificial Intelligence, Machine Learning, and Natural Language Processing

Our group members study and devise core machine learning and artificial intelligence methods to solve complex problems throughout science, engineering, and medicine. Our goal is to enhance human lives and bring advanced technologies to augment human capabilities. This research involves both deployments in real-world applications as well as development of fundamental theories in computer science, mathematics, and statistics.  

List of Faculty

• Aniket Bera
• Simina Branzei
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• Berkay Celik
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• Dan Goldwasser*
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• Sooyeon Jeong
• Rajiv Khanna*
• Anuran Makur
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• Jianguo Wang
• Yexiang Xue*
• Raymond Yeh*
• Ruqi Zhang*
• Tianyi Zhang

(* indicates primary area of research)

Related Links

• Co gnitive  R obot  A utonomy and  L earning (CoRAL) lab
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Bioinformatics and Computational Biology

Faculty in the area of bioinformatics and computational biology apply computational methodologies such as databases, machine learning, discrete, probabilistic, and numerical algorithms, and methods of statistical inference to problems in molecular biology, systems biology, structural biology, and molecular biophysics.

• Bedrich Benes
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Sample Projects

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Computer Architecture

Computer Architecture research studies the interplay between computer hardware and software, particularly at the intersection of programming languages, compilers, operating systems, and security.

• Changhee Jung
• Xuehai Qian*
• Kazem Taram*

Computational Science and Engineering

The research area of Computational Science and Engineering answers questions that are too big to address experimentally or are otherwise outside of experimental abilities. Using the latest computers and algorithms, this group addresses those questions through numerical modeling and analysis, high-performance computation, massive distributed systems, combinatorial algorithms in science applications, high-speed data analysis, and matrix-based computations for numerical linear algebra.

• Petros Drineas*
• David Gleich*
• Ananth Grama*
• Alex Pothen*
• Ahmed Qureshi
• Elisha Sacks
• Xavier Tricoche
• Yexiang Xue

CSE Research Group

• David Gleich Software Artifacts
• Finite Element Analysis of 9/11 Attacks

Databases and Data Mining

The data revolution is having a transformational impact on society and computing technology by making it easier to measure, collect, and store data. Our databases and data mining (big data) research group develops models, algorithms, and systems to facilitate and support data analytics in large-scale, complex domains.  Application areas include database privacy and security, web search, spatial data, information retrieval, and natural language processing.

• Walid Aref*
• Elisa Bertino
• Bharat Bhargava*
• Chris Clifton*
• Dan Goldwasser
• Susanne Hambrusch
• Jennifer Neville
• Sunil Prabhakar*
• Bruno Ribeiro
• Jianguo Wang*
• Cyber Space Security Lab (CyberS2Lab)
• Conceptual Evaluation and Optimization of Queries in Spatiotemporal Data Systems
• Secure Dissemination of Video Data in Vehicle-to-Vehicle Systems
• Ensuring Integrity and Authenticity of Outsourced Databases
• Towards Scalable and Comprehensive Uncertain DAta Management
• ORION DBMS: Handling Nebulous Data

Distributed Systems

The DS group focuses on designing distributed systems that are scalable, dependable, and secure, behaving according to their specification in spite of errors, misconfigurations, or being subjected to attacks. Areas of focus include virtualization technologies with emphasis on developing advanced technologies for computer malware defense and cloud computing.

• Bharat Bhargava
• Pedro Fonseca
• Suresh Jagannathan
• Aniket Kate
• Kihong Park
• Vernon Rego*
• Eugene Spafford
• Yongle Zhang
• Vassilis Zikas
• Saurabh Bagchi (by courtesy)
• Charlie Hu (by courtesy)
• Sanjay Rao (by courtesy)

  (* indicates primary area of research)

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Graphics and Visualization

This group performs research in graphics, visualization, computational geometry, and related applications.  Focus areas include model acquisition, image generalization, scientific visualization, urban modeling, robust computational geometry, and geometric computations and constraints.

• Daniel Aliaga *
• Bedrich Benes*
• Voicu Popescu*
• Elisha Sacks*
• Xavier Tricoche*
• Computer Graphics and Visualization Lab
• High Performance Computer Graphics Laboratory

Graphics Lab Projects

Human-Computer Interaction

• Sooyeon Jeong*
• Tianyi Zhang*

Information Security and Assurance

Strong security and privacy is needed to defend our records, communications, finances, governments and infrastructure against all manner of threats and attacks, while also enhancing legitimate uses. Research in Information Security and Assurance focuses on the analysis, development, and deployment of technologies, algorithms, and policies to protect computing and data resources against malicious access or tampering, and to validate authenticity. 

• Mikhail Atallah*
• Elisa Bertino*
• Antonio Bianchi*
• Jeremiah Blocki*
• Berkay Celik*
• Sonia Fahmy
• Christina Garman*
• Aniket Kate*
• Ninghui Li*
• Hemanta Maji*
• Sunil Prabhakar
• Vernon Rego
• Eugene Spafford*
• Dongyan Xu*
• Vassilis Zikas*
• Freedom Research Lab
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• Spatial-temporal Recreation of Android App Displays from Memory Images
• Multiple Perspective Attack Investigation with Semantic Aware Execution Partitioning
• HexHive Group Projects
• Chunyi Peng Mobile Phone Projects
• Freedom Lab Projects

Networking and Operating Systems

This area works on fundamental problems at different layers of the network protocol stack – from the medium access control layer up to the application layer – using theoretical models, simulation, emulation, and extensive testbed experimentation to develop and evaluate proposed solutions which leverage techniques from game theory, information theory, complexity theory, optimization, and cryptography.

• Saurabh Bagchi*
• Antonio Bianchi
• Doug Comer*
• Sonia Fahmy*
• Pedro Fonseca*
• Kihong Park*
• Chunyi Peng*
• Muhammad Shahbaz*
• Yongle Zhang*

Programming Languages and Compilers

The PL group engages in research spanning all aspects of software systems design, analysis, and implementation.  Active research projects exist in functional and object-oriented programming languages, both static and dynamic compilation techniques for scalable multicore systems, generative programming, assured program generation, scripting languages, distributed programming abstractions and implementations, real time and embedded systems, mobile and untrusted computing environments, and runtime systems with special focus on memory management and parallel computing environments.

• Ben Delaware*
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• Changhee Jung*
• Zhiyuan Li*
• Ryan Newton*
• Tiark Rompf*
• Roopsha Samanta*
• Xiangyu Zhang*
• Yung-Hsiang Lu (by courtesy)
• Milind Kulkarni (by courtesy)
• PurForM  - Purdue's Formal Methods research group

PurPL - Center for Programming Principles and Software Systems

• Secure Software Systems Lab (S3)

Software Engineering

The software engineering area conducts research on applying advanced program analyses towards problems related to fault isolation and various kinds of bug detection, including those related to race conditions in concurrent programs, and specification inference for large-scale software systems.

• Ben Delaware
• Buster Dunsmore*
• Xiangyu Zhang

Automatic Model Generation from Documentation for Java API Functions

Robotics and Computer Vision

The Robotics and Computer Vision area includes elements of machine learning, signal processing, and image processing to further develop robotics and computer vision systems from a computational science perspective.

• Aniket Bera*
• Raymond Yeh

Theory of Computing, Algorithms, and Quantum Computing

Members of the group work in areas that include analysis of algorithms, parallel computation, computational algebra and geometry, computational complexity theory, digital watermarking, data structures, graph algorithms, network algorithms, distributed computation, information theory, analytic combinatorics, random structures, external memory algorithms, and approximation algorithms.

• Mikhail Atallah
• Saugata Basu*
• Jeremiah Blocki
• Simina Branzei*
• Brian Bullins
• Elena Grigorescu*
• Susanne Hambrusch*
• Steve Hanneke
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• Hemanta Maji
• Anuran Makur*
• Alex Psomas*
• Kent Quanrud*
• Eric Samperton*
• Wojtek Szpankowski*
• Paul Valiant*
• Sabre Kais  (by courtesy)

Theory Group

CGTDA: Computational Geometry & Topology  for Data Analysis

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The computing and information revolution is transforming society. Cornell Computer Science is a leader in this transformation, producing cutting-edge research in many important areas. The excellence of Cornell faculty and students, and their drive to discover and collaborate, ensure our leadership will continue to grow.

The contributions of Cornell Computer Science to research and education are widely recognized, as shown by two Turing Awards, two Von Neumann medals, two MacArthur "genius" awards, and dozens of NSF Career awards our faculty have received, among numerous other signs of success and influence.

To explore current computer science research at Cornell, follow links at the left or below.

Research Areas

Knowledge representation, machine learning, NLP and IR, reasoning, robotics, search, vision

Statistical genetics, sequence analysis, structure analysis, genome assembly, protein classification, gene networks, molecular dynamics

Computer Architecture & VLSI

Processor architecture, networking, asynchronous VLSI, distributed computing

Database systems, data-driven games, learning for database systems, voice interfaces, computational fact checking, data mining

Interactive rendering, global illumination, measurement, simulation, sound, perception

HCI, interface design, computational social science, education, computing and society

Artificial intelligence, algorithms

Programming language design and implementation, optimizing compilers, type theory, formal verification

Perception, control, learning, aerial robots, bio-inspired robots, household robots

Numerical analysis, computational geometry, physically based animation

Secure systems, secure network services, language-based security, mobile code, privacy, policies, verifiable systems

The software engineering group at Cornell is interested in all aspects of research for helping developers produce high quality software.

Operating systems, distributed computing, networking, and security

The theory of computing is the study of efficient computation, models of computational processes, and their limits.

Computer vision

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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.

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

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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

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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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Our world-class research faculty and students work in a range of areas crucial to the future of the world economy and human thriving. We bring computing innovations to the world and nuture the next generation of computer scientists. Find faculty by their research focus below.

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Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

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Doctoral thesis: guiding deep probabilistic models, doctoral thesis: designing for participation and power in data collection and analysis.

• United Kingdom
• Swansea University
• Posted on: 4 June 2024

Computer Science: Swansea University Research Excellence Scholarships (SURES): Fully Funded PhD Scholarship: Enumeration degrees and topology

Job information, offer description.

Funding provider:  Swansea University

Subject areas:  Computer Science / Mathematics

Project start date:

• 1 October 2024 (Enrolment open from mid-September)

Supervisor:  Dr Arno Pauly ( [email protected] )

Aligned programme of study:  PhD in Computer Science

Mode of study:  Full-time

Project description:  

Research at the Faculty of Science and Engineering

Kihara and Pauly recently uncovered an intricate connection between computability theory and topology (“Point degree spectra of represented spaces”, Forum of Mathematics Sigma 2022). They associate a degree structure (called degree spectrum) to a topological space which reflects many of its topological characteristics. For example, the real numbers are associated with the Turing degrees. This connection already has been used to obtain new results in topology using computability-theoretic methods and vice versa, including solving a long-open question by Jayne on the number of sigma-homeomorphism types of Polish spaces. This project is about further exploring this connection.

Concrete research objectives include calculating the degree spectra of more topological spaces exhibiting unusual properties, and to identify topological properties linked to classes of degrees such as the uniformly intro-enumerable ones. A big open question in this area is whether every almost-total enumeration degree is graph-cototal.

Background knowledge in some of pointset topology, computability theory, dimension theory or logic would be helpful.

Where to apply

Requirements.

Candidates must have attained, or must be expected to attain, a first-class honours degree and/or a distinction at master’s level. If you are eligible to apply for the scholarship but do not hold a UK degree, you can check our comparison entry requirements. Please note that you may need to provide evidence of your English Language proficiency. 

• Where applicants have multiple master’s degrees, a distinction must be held in the degree that is most relevant to the intended PhD study. 
• If you are currently studying for a master’s level qualification with an expected award date that is later than 01/10/2024, you should hold a minimum of an upper-second-class (2:1) honours degree.   
• You should be able to demonstrate a pass with a minimum grade average of at least 70% for your part-one master’s degree modules (the taught aspect of your master’s course rather than a research-focused dissertation) and submit your dissertation by no later than 30/09/2024.  

Applicants must be able to begin their course of study in October 2024. As a cohort-based programme, deferral to an alternative enrolment window within the academic year or to another academic year is not permissible.

Please note that both the degree and language-proficiency entry requirements for SURES are higher than the baseline standard for entry that is stipulated for most of the PhD programmes at Swansea University. 

Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only , as defined by  UKCISA regulations .  

Additional Information

This scholarship covers the full cost of UK tuition fees and an annual stipend of £19,237.

Additional research expenses will also be available.

Please visit our website for more information.

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• UTCS Direct

UT Computer Science Students Win Prestigious NSF Graduate Research Fellowships

Submitted by Staci R Norman on Mon, 05/20/2024 - 10:00am

The National Science Foundation (NSF) has announced the recipients of its prestigious Graduate Research Fellowships (NSF GRFP) for 2024, and students from the Department of Computer Science at The University of Texas at Austin's College of Natural Sciences (CNS) have been prominently recognized. This year, four Computer Science students were honored with fellowships or honorable mentions, highlighting their outstanding contributions and potential in various cutting-edge research areas.

Fellowship Recipients

• Leo Orshansky, undergraduate student  - Unconventional Computing, Quantum Computing
• Stephane Hatgis-Kessell, undergraduate  - Artificial Intelligence

Honorable Mentions

• Arthur King Zhang, graduate student - Robotics
• Jason Ho, graduate student - Computer Architecture
• Jacob L. Block, graduate student - Machine Learning

College-Wide Recognition

Aside from the accomplishments within the Computer Science department, a total of six undergraduate students and nine graduate students across various departments within the College of Natural Sciences were awarded NSF Graduate Research Fellowships. Additionally, two undergraduates and twelve graduate students received honorable mentions. These awardees represent a broad spectrum of research fields, including Mathematics, Chemistry, Physics, Biology, Marine Science, and Astronomy.

In total, 15 students from seven different departments within CNS were honored with fellowships, underscoring the diverse and high-caliber research being conducted at The University of Texas at Austin.

About the NSF Graduate Research Fellowship Program

The NSF GRFP is a highly competitive program that supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics (STEM) disciplines. Fellows receive a three-year annual stipend of $37,000 along with a $16,000 cost-of-education allowance, providing significant support to pursue their research at any accredited U.S. graduate institution.

These fellowships not only recognize the exceptional talents and research potential of the students but also contribute to the advancement of knowledge and technological innovation critical to the nation's economic and social well-being. The achievements of the Computer Science students, along with their peers from other departments, highlight the University of Texas at Austin's role as a leader in scientific research and education.

Adapted from an announcement by the College of Natural Sciences .

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School of Data Science to launch Ph.D. program, formally joins College of Computing and Informatics

UNC Charlotte’s School of Data Science will soon expand its academic offerings with the establishment of a Doctor of Philosophy in Data Science. Approved by the UNC System Board of Governors in May 2024, the degree program will enroll its first cohort of students in fall 2025, pending approval of the Southern Association of Colleges and Schools Commission on Colleges. 

Established in response to the skyrocketing growth of the data science industry in North Carolina and globally, the new doctorate will offer two pathways for students, providing training for both future industry practitioners and university educators. This transdisciplinary program will emphasize the core technical skills of machine learning, artificial intelligence and statistics along with the social implications and ethics of data use. The program builds on the Master of Science and Bachelor of Science programs offered by the school. Its establishment is the latest example of the University’s commitment to data science, coming over 10 years after the founding of UNC Charlotte’s Data Science Initiative.

 “UNC Charlotte is always working to add and evolve academic programs with an eye toward the future. The creation of the new data science doctoral program is the latest example of our ongoing efforts to align our curriculum to the demands of industry and will allow our thriving School of Data Science to further build on its track record of interdisciplinary innovation,” said Jennifer Troyer, provost and vice chancellor for academic affairs.

The new Ph.D. will become UNC Charlotte’s 25th doctoral degree program.

A new college home and transition in leadership The doctoral program will be enhanced by the School of Data Science formally joining the University’s College of Computing and Informatics . 

This new structure will support and amplify the school’s ongoing mission to foster interdisciplinary research and partnership across the University, all while providing SDS with the institutional structure necessary for continued faculty expansion, student growth and research excellence as the school continues to bolster its position as an innovative data science institution working to push the field forward. The shift also will allow SDS and CCI to more effectively support the North Carolina General Assembly’s $41.2 million investment toward “Engineering North Carolina’s Future,” in service of the initiative’s call for investing in data science, cybersecurity and engineering efforts across the state.

As part of this transition, the school’s current Director of Research Dongsong Zhang was named its new interim executive director, effective May 15. Zhang is the Belk Endowed Chair in Business Analytics in the Belk College of Business. Founding Executive Director Doug Hague will work closely with Zhang throughout the upcoming academic year during the transition, as Hague begins a new role as executive director of corporate engagement for UNC Charlotte. In his new position, Hague will work hand-in-hand with University leadership, the Division of University Advancement and external partners to build relationships that strengthen UNC Charlotte’s connection with the business community and create additional opportunities for collaboration.

“With the newly approved data science doctoral program and the evolution of the School of Data Science’s relationship with the College of Computing and Informatics, SDS continues to strengthen its position as a leading data science program,” said Bojan Cukic, dean of the College of Computing and Informatics. “I am excited to continue to work alongside Dongsong Zhang in the months ahead as he and his team work to chart the school’s future. We are also extremely grateful for Doug Hague’s bold, thoughtful leadership of SDS over the years. He has played an instrumental role in the school’s incredible success, and we look forward to continued partnership with him in his new role as he works to foster innovation and industry collaboration to the benefit of our University.”

The UNC Charlotte Data Science Initiative was established in 2013. That initiative ultimately grew into the School of Data Science, founded in 2020. The Carolinas’ first School of Data Science, SDS is committed to excellence in education, research and community engagement to shape and lead the future of data science education, research and practice. Since its inception and to this day, the School of Data Science is an interdisciplinary partnership among UNC Charlotte’s College of Computing and Informatics, the Belk College of Business, the College of Humanities & Earth and Social Sciences, the College of Science, the College of Health and Human Services and the William States Lee College of Engineering.

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