PhD Program Requirements
The PhD students are required to complete:
- Six breadth courses
- CSC 400: Problem Seminar
- All of the qualifying requirements for one of the following areas:
- PhD thesis proposal and proposal defense
- PhD dissertation and defense
Breadth courses are divided into three groups as follows:
AI and HCI
- CSC 412: Human-Computer Interaction
- CSC 440: Data Mining
- CSC 442: Artificial Intelligence (*)
- CSC 444: Logical Foundations of Artificial Intelligence
- CSC 446: Machine Learning
- CSC 447: Natural Language Processing
- CSC 448: Statistical Speech and Language Processing
- CSC 449: Machine Vision
- CSC 451: Advanced Computer Architecture
- CSC 452: Computer Organization (*)
- CSC 453: Dynamic Languages and Software Design
- CSC 454: Programming Language Design and Implementation
- CSC 455: Software Analysis and Improvement
- CSC 456: Operating Systems
- CSC 457: Computer Networks
- CSC 458: Parallel and Distributed Systems
- CSC 480: Computer Models and Limitations (*)
- CSC 481: Introduction to Cryptography
- CSC 482: Design and Analysis of Efficient Algorithms (*)
- CSC 483: Topics in Cryptography
- CSC 484: Advanced Algorithms
- CSC 485: Algorithms and Elections
- CSC 486: Computational Complexity
- CSC 487: Advanced Modes of Computation
Students must complete two courses from each group. These breadth courses must be taken in the first or second year of graduate study. At least one course in each group must be taken in the first year. At most two starred (*) courses, total, can be used toward the breadth requirement. Students have the flexibility to complete all of the breadth requirements during their first year, or to complete as few as one course per group in that year in order to allow more time for research.
PhD students are expected to earn a B or better in all breadth courses.
In addition to the breadth requirements, PhD students must qualify in a research area. All research areas require writing and defending an area paper during the second year of graduate study. Each area also requires additional course work, beyond the breadth requirement.
A Master’s degree is offered to those PhD-program students passing the area paper with an appropriate level of performance or, in special cases, passing an alternative comprehensive examination given by two or more faculty members appointed by the Chair.
Area specific requirements are as follows:
AI Area Process—The student should form a committee of three or more AI faculty members before the area paper is due, and schedule a time and date for the exam so that all committee members can attend.
The paper should include a survey of a research topic and initial original research.
The defense begins with the student providing a 20 minute overview of the area paper, followed by an hour of intensive questioning by faculty members.
Questions from faculty members will include:
- General questions about AI
- Questions covering content learned in AI breadth courses taken by the student
- Questions about the area paper
Students are responsible for preparing themselves for these questions.
AI Course Requirements—By the end of the fourth year, the student must complete at least two additional graduate-level AI/HCI courses. These may include the non-starred entries of the AI/HCI breadth list, BCS 505 (Perception and Motor Systems), BCS 512 (Computational Methods in Cognitive Science), or other advanced courses with permission of the AI faculty.
HCI Area Process—The area paper may be a survey of a research topic, original research, or a combination of both.
Following a public presentation of the paper, the student will answer questions from the committee about the paper and about any topic in HCI that is broadly relevant to the research topic.
HCI Course Requirements—By the end of the fourth year, the student must complete three additional courses, with one class drawn from each of the following three clusters.
i. Core AI Cluster
1. CSC578 - Deep Learning
2. CSC447 - Natural Language Processing
3. CSC576 - Advanced Machine Learning and Optimization
4. CSC444 - Logical Foundations of Artificial Intelligence
5. CSC440 - Data Mining
ii. Statistics Cluster:
1. CSC462 - Computational Introduction to Statistics
2. CSC465 - Intermediate Statistical Methods
iii. Special Topics Cluster:1. Network Sciences
B. ECE442 - Network Science Analytics
C. ECE440 - Introduction to Random Processes
A. CSP550 - Social Psychology of Emotion
B. CSP557 - Affectation Bases of Behavior
Systems Area Process—The area paper should be original MS-level research and a survey of related research topics.
Approximately two weeks prior to the scheduled area exam, the systems faculty will provide the student with a small set of "take home" papers to be studied for the exam. Following a public presentation of the area paper, the student will answer questions from the faculty about the area paper, the take-home papers, and background knowledge from systems courses.
Systems Course Requirements—By the end of the fourth year, the student must complete at least two additional graduate-level systems courses. These may include the non-starred entries of the systems breadth list or other advanced courses with permission of the systems faculty.
Theory Area Process —The area process including the area defense is organized as follows:
- The student submits an area paper reflecting research ability.
- Approximately two weeks before the oral exam, the student is given a set of “take home” papers.
- Approximately three hours before the oral exam, the student is given a written set of “morning questions.”
- The area defense consists of an oral exam covering the area paper, the take home papers, the morning questions, and other area-related questions. The length of the exam is typically between 2 and 3 hours.
Theory Course Requirements—By the end of the fourth year, the student must complete one additional non-starred course from the theory breadth list.
A more detailed written description of the logistics of this process is available from the graduate coordinator or any theory faculty member.
See PhD Thesis Proposal.
As in programs everywhere, the culmination of the PhD program is a body of research that significantly advances the frontiers of human knowledge, the presentation of that research in a dissertation meeting the highest standards of academic scholarship, and an oral exam that successfully defends the dissertation before a committee of established scholars in the field. For further information, consult
- your thesis advisor
- the University's Graduate Studies Bulletin
- Regulations and University Policies Concerning Graduate Studies
- The University's Guide to the Final Defense