May 24, 2026  
Graduate Record 2026-2027 
    
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Graduate Record 2026-2027

Computer Science, Ph.D.

 Return to: School of Graduate Engineering and Applied Science: Degree Programs    

COMPUTER SCIENCE PH.D. DEGREE

Computer Science
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Ph.D. students are expected to gain in-depth expertise in a specific area of computer science. To achieve this, students start working with faculty advisors during their first semester, attend seminars, participate in professional conferences, and submit refereed publications throughout the program. In addition to specific course requirements, students are expected to develop essential skills for well-founded research, engage in department activities, and regularly attend colloquia and seminars. Ph.D. students receive funding through fellowships or research assistantships. 

 

Ph.D. Student Assessments 

Each Ph.D. student’s progress and achievements will be assessed twice a year by their faculty advisor and a three-person faculty committee. The results of this assessment will be shared with both the student and the faculty advisor. For more details, please refer to the “Computer Science Graduate Student Handbook.”

 

Ph.D. Qualification Committee 

This committee must consist of the student’s advisor(s) and a minimum of three Computer Science (CS) faculty members, with at least two CS faculty who are not the student’s advisor(s). The committee must have an explicitly designated chair who will direct meetings and ensure proper procedure. The advisor(s) may not serve as the chair. 

CS faculty are defined as those with primary or secondary appointments in Computer Science. Courtesy appointments in CS do not count toward the required number of CS faculty members on the committee.

 

Ph.D. Doctoral Committee

Students should consult “Committee Requirements” in the School of Engineering and Applied Science—Academic Rules section. The policies detailed here complement the SEAS Requirements and clarify their applications to Computer Science.   

The PhD committee (Dissertation Proposal and Dissertation Defense) should be arranged by the student after the qualifying exam. The committee must consist of a minimum of five faculty members. Membership must include at least three Computer Science faculty members, at least one UVA faculty member from outside the Computer Science department and at least one other member with expertise in the research area, i.e. 3 CS faculty + 1 UVA faculty (outside CS) + 1 outside expert. The Department recommends that one of the committee members be an expert from outside the University. CS faculty are defined as those with primary or secondary appointments in CS. Faculty with courtesy appointments in CS do not count toward the required number of CS faculty members on the committee. 

 

Ph.D. Degree Requirements - 72 graduate-level credits:

  • 3 credits of CS 6190 Perspectives (required) in the first semester. This course is coordinated with, and the course grade is in part conditioned upon, performance in the First-Year Rotation.
  • 12 credits of graded, graduate-level CS breadth electives comprised of a minimum of 3 credits (graduate-level 6000 and above) in any four of the six focal areas (tracks) listed below. The breadth requirement is the same for Ph.D. and M.S.
  • 12 credits of graded, graduate-level CS electives (graduate-level 6000 and above) or other graduate courses approved by the advisor and the PhD Graduate Program Director (PGPD). 
  • 12 credits of CS 8897/9897 (Graduate Teaching Assistant)
  • 33 credits of CS 8999/CS 9999 (Research) 
  • Completion of the Qualifying Examination
  • Completion of the PhD Dissertation Proposal
  • Completion of the Oral Defense of the written Dissertation

 

General Notes:

  • A graded credit means that the course resulted in a letter grade (A, B, C, etc.) 
  • No grade lower than a “B” will be accepted towards satisfying the PhD degree requirements (graduate-level 6000 and above). While a course with a passing grade lower than B will count in the GPA, it will not count toward degree requirements.
  • At most 3 credits of CS 6993/7993 (Independent Study) may count toward the degree.
  • None of CS 8897/9897 (Graduate Teaching Instruction), CS 8999 (Thesis), CS 9999 (Dissertation) or any English as a Second Language (ESL) course can be used to satisfy this 24-credit coursework (12 breadth + 12 electives) requirement.
  • If a student transfers a STEM Master’s degree and receives 24 “bulk transfer” credits, then 6 additional credits of CS coursework taken at UVA are required. These credits cannot be satisfied via transfer.
  • Coursework should be chosen from among our CS graduate courses. Non-CS courses may be approved on a case-by-case basis by the student’s academic advisor and the PGPD.

Transfer Credits

Students should consult “Transfer Credits” in the School of Engineering and Applied Science—Academic Rules section of the Record for information about transferring courses toward their graduate degree. Whether any individual transfer course counts toward CS Ph.D. degree requirements is determined by the PGPD.

 

 

Breadth Areas & Courses (6000 level and above)

 

1. Cyber Physical Systems, Internet of Things, Embedded Systems

2. Machine Learning, Natural Language Processing, Information Retrieval, Text Mining, Data Mining

  • Credits: 3
  • Credits: 3

  •       Approved Topic: Trustworthy AI (Zeng)

          Approved Topic: AI for Digital Health (Nepal)

          Approved Topic: Constrained-Aware Generative AI for Sci & Engr (Fioretto)

          Approved Topic: Workshop on Building AI Agents (Kautz)

          Approved Topic: Statistical Learning and Graphical Models (Hassanzadeh)

          Approved Topic: Geometry of Data (Fletcher)

          Approved Topic: AI for Social Good (Doryab)

          Approved Topic: Machine Learning in Image Analysis (Zhang)

          Approved Topic: Interpretable Machine Learning (Ji)

          Approved Topic: Learning in Robotics (Behl)

          Approved Topic: Topics in Reinforcement Learning (Zhang)

          Approved Topic: Digital Signal Processing (Fletcher)

          Approved Topic: Program Analysis for ML and ML for Prog Analysis (Elbaum)

          Approved Topic: Learning for Interactive Robots (Kuo)

          Approved Topic: Risks and Benefits of Generative AI and LLMs (Evans, Qi)

          Approved Topic: Responsible AI: Privacy, Fairness, and Robustness (Fioretto)

          Approved Topic: Probabilistic Machine Learning (Farnoud)

          Approved Topic: 3D Computer Vision (Cheng)

          Approved Topic: Graph Machine Learning (Chen)

          Approved Topic: Machine Learning for Software Reliability (Wang)

          Approved Topic: Neural Networks (Daneshmand)

          Approved Topic: Analyzing Online Behavior for Public Health (Kautz)

          Approved Topic: Machine Learning on Graphs (Li)

  • Credits: 3
  • Credits: 3
  • Credits: 3

  •          Approved Topic: Advanced Topics in Machine Learning (Ji)

  • Credits: 3

3. Security, Privacy, Cryptography

  • Credits: 3
  • Credits: 3
  • Credits: 3

  •       Approved Topic: Security of AI Systems: Attacks & Defenses (Ul Hassan)

          Approved Topic: Software Security via Program Analysis (Kwon)

          Approved Topic: Cryptography (Mahmoody)

          Approved Topic: Software Security (Kwon)

          Approved Topic: Cyber Forensics: Automated Software Approaches (Kwon)

          Approved Topic: Hardware Security (Venkat)

          Approved Topic: Network Security and Privacy (Sun)

          Approved Topic: Data Privacy (T. Wang)

          Approved Topic: Threat Detection and Response (Ul Hassan)

          Approved Topic: Responsible AI: Privacy, Fairness, and Robustness (Fioretto)

          Approved Topic: Risks and Benefits of Generative AI and LLMs (Evans, Qi)

          Approved Topic: Economics of Distributed Systems (Ferreira)

          Approved Topic: Software Security Testing (Davidson)

          Approved Topic: Machine Learning in Systems Security (Ul Hassan)

4. Theory and Algorithms

5. Computer Systems

  • Credits: 3
  • Credits: 3
  • Credits: 3
  • Credits: 3
  • Credits: 3
  • Credits: 3

  •       Approved Topic: Networking Infrastructure Within Data Centers (Cai)

          Approved Topic: Serverless AI (Cheng)

          Approved Topic: Hardware Security (Venkat)

          Approved Topic: Computer Networks (Sun)

          Approved Topic: Network Security and Privacy (Sun)

          Approved Topic: Advanced Embedded Computing Systems (Alemzadeh)

          Approved Topic: Computer Architecture: Hardware Accelerators (Skadron)

          Approved Topic: Software-Defined Networking and Prog Networks (Kim)

          Approved Topic: Cloud System Reliability (Lou)

          Approved Topic: Modern Computing Architectures (Jog)

          Approved Topic: GPU Architectures (Jog)

          Approved Topic: CPU/GPU Memory Systems and Near-Data Processing (Skadron)

6. Software Engineering

Total Required Credits: 72