Nov 21, 2024  
Graduate Record 2023-2024 
    
Graduate Record 2023-2024 [ARCHIVED RECORD]

Computer Science, Ph.D.


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


COMPUTER SCIENCE PH.D. DEGREE


Graduate students are expected to master one area of computer science in depth. To this end, each new student chooses a research advisor within the first semester, takes several advanced seminars, participates in professional conferences, and submits refereed publications during their tenure here. Although specific course requirements are minimal for the Ph.D. degree, students in the program are expected to develop skills necessary for well-founded scientific research, participate in the ongoing intellectual life of the department, and regularly attend colloquia and seminars. PhD students are funded by assistantships or fellowships.

Assessment: Each semester the Department conducts a review of each Ph.D. student’s progress along these steps. Faculty are asked to evaluate each student’s performance and deadlines for each step and to provide documentation substantiating the evaluation. The Ph.D. Graduate Program Director normally consults with those students who receive a rating of fair or poor to determine what can be done to improve performance. If marginal performance continues, the student may be asked to leave the Ph.D. program. 


 

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. 
  • 12 credits of CS 8897/9897 (Graduate Teaching Assistant Requirement)
  • 33 credits of CS 8999 (Thesis, taken pre-qualifying exam) and CS 9999 (Dissertation, taken after passing the qualifying exam) 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 PhD Graduate Program Director. (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
  • Deep Learning for Visual Recognition 

    Learning Theory 

    Statistical Learning and Graphical Models 

    Tensors for Data Science 

    Natural Language Processing 

    Data Mining -  Principles and Algorithms 

    Mining Text Data for Knowledge Discovery 

    Reinforcement Learning  

    Vision & Language 

    Topics at the Interface of Learning and Game Theory 

    Information Retrieval 

    Geometry of Data 

    Digital Image Processing  

    AI for Social Good  

    Machine Learning in Image Analysis  

    Interpretable Machine Learning 

    Learning in Robotics 

    Topics in Reinforcement Learning 

    Digital Signal Processing 

  • Credits: 3
  • Credits: 3
  • Advanced Natural Language Processing 

    Advanced Topics in Deep Learning 

    Advanced Topics in Machine Learning 

3. Security, Privacy, Cryptography


4. Theory and Algorithms


5. Computer Systems


6. Software Engineering