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  5. Doctor of Philosophy

Doctor of Philosophy

PhDJoint Degree
  • Computer Science opens in a new tab
Program Minimum Units
135 Units
University Minimum Units
135 Units
Program Length
6 Years
Interests
Technology, Business & Entrepreneurship
Academic Areas
Engineering & Computer Science

Overview

Doctor of Philosophy in Computer Science

The PhD degree is intended primarily for students who desire a career in research, advanced development, or teaching. For this type of work, a broad background in computer science and the engineering sciences, and intensive study and research experience in a specialized area are the necessary requisites.

The degree of Doctor of Philosophy (PhD) is conferred on candidates who have demonstrated to the satisfaction of our department:

  • High attainment in a particular field of knowledge.

  • Ability to do independent investigation and present the results of research.

They must satisfy the general requirements for advanced degrees, the program requirements specified, and the doctoral requirements for candidacy, as outlined in our department.

Guidelines for Reasonable Progress

  • By the end of the first academic year, you should align with a permanent advisor. Students are welcome to switch advisors; however, a student should not have significant periods of time (after the first year) with no advisor.

  • A student must make satisfactory progress in their research, as determined by their advisor. Participation in research throughout the student's career routinely satisfies the research requirement.

  • Three foundation/breadth requirements must be completed by spring quarter of the second year.

  • A student should meet the eligibility requirements and file for Candidacy by the end of the second year in their program.

  • All courses must be completed by the end of the third year. Any deviation from this timeline must be approved in advance by both the student advisor and the director of the PhD program.

  • By spring quarter of the third year, a student should pass a Qualifying Examination opens in a new tab in the area of their intended dissertation.

  • Within one year of passing the Qualifying Examination, a student should form a reading committee and submit a signed Reading Committee form to the PhD Student Services Office at phdstudentservices@cs.stanford.edu.

  • By spring quarter of the fourth year, a student should schedule the Thesis Proposal with the reading committee members and submit the Thesis Proposal form opens in a new tab to the PhD Student Services Office at phdstudentservices@cs.stanford.edu.

  • The Teaching Requirement must be completed by their fourth year. As a matter of policy, students are required to satisfy half of the teaching requirement within the first two years of their program. Students are encouraged to complete the teaching requirement early in their stay to eliminate conflicts with later dissertation work.

  • The University Oral Examination must be completed at least a quarter before the degree is conferred.

Requirements

Program Requirements

Requirements Overview

The following are general department requirements. Contact the Computer Science PhD administrator for details.

  • A student should plan and complete a coherent program of study covering the basic areas of computer science and related disciplines. The student’s advisor is primarily responsible for the program’s adequacy, which is subject to review by the CS PhD Student Services Office.

  • The first year of the PhD program is spent working with one to three different professors on a rotating basis. The intent is to allow the first-year PhD student to work with various professors before aligning with a permanent program advisor. Students who don’t need the entire year to find a professor to align with will have the option of aligning within the first or second quarter.

  • The CS 300 Departmental Lecture Series seminar allows faculty to explain their research to first-year CS PhD students. First-year CS PhD students must attend 2/3 of faculty-led sessions in order to receive credit.

    • A student must complete 135 course units for graduation (a total of 10 units of PE courses can be counted towards this). Computer Science PhD students take eight to ten units per quarter. Credit for coursework completed elsewhere (up to a maximum of 45 course units) may be applied toward graduation requirements. Students must also complete at least three units of coursework with at least four different faculty members. There are NO courses explicitly required by the CS PhD program except for the 1 unit CS 300 Departmental Lecture Series and at least three (3) CS499 opens in a new tab (Advanced Reading and Research) or its equivalent. At least one course must be taken for a letter grade. A 3.0 GPA must be maintained.

  • Students must satisfy the foundation/breadth requirements to remain in the PhD program. The department provides a clear roadmap for these requirements, including the option to take or waive the following foundation courses: CS103, 109, 111, and 161. The breadth course requirement will be three courses from four predefined areas and cannot be waived. A student needs to pass the courses with a grade of B+ or higher. A student must fulfill three foundation/breadth requirements by the end of year two and complete all foundation/breadth requirements by the end of the third year.

  • Once these requirements are met, and a student has completed at least a three-unit graduate-level course with each of four instructors who are members of the Academic Council, they can apply for candidacy by the end of their second year in the program. The department’s website has an up-to-date course list that satisfies the breadth requirements.

  • The student must pass a qualifying exam in the general area of their expected dissertation by the end of the third year in the program, ensuring a clear and structured progression.

Departmental Lecture Series

First-year CS PhD students must attend two-thirds of faculty-led sessions in order to receive credit for the class.

Complete all of the following course(s):
CS 300

Departmental Lecture Series

1 Units

To be taken in the first quarter of the student’s first year.

Advanced Reading and Research

Complete any of the following course(s):
CS 499

Advanced Reading and Research

1-15 Units
CS 499P

Advanced Reading and Research

1-15 Units

Or its equivalent.

All PhD students must enroll in at least three units of CS 499/CS499P or equivalent  (Research units with your non-CS faculty advisor’s home department) for all quarters.

Foundation Course Requirements

Students will be required to take or waive CS 103, 109, 111, 161.

Complete all of the following course(s):
CS 103

Mathematical Foundations of Computing

3-5 Units
CS 109

Introduction to Probability for Computer Scientists

3-5 Units
CS 111

Operating Systems Principles

3-5 Units
CS 161

Design and Analysis of Algorithms

3-5 Units
  • Completion of a CS Bachelor’s degree and/ or CS Master’s degree automatically waives all four courses.

  • Waiving will be lenient based on sufficient coverage of a predefined set of topics for each course and transparent.

  • Double dipping: At the discretion of the waiver approver, a specific, more advanced Stanford course could be assigned to waive foundation courses (e.g., CS 154 for CS 103), allowing students to fill both foundation and breadth requirements simultaneously.

*Note: Units from waived courses to satisfy the foundation requirements will not count toward candidacy. If you have waived all the foundation requirements due to your CS bachelor's or master's Degree, those units will not count toward candidacy.

Breadth Course Requirements

  • The breadth course requirement will be three (3) total courses from four (4) predefined areas (cannot be two (2) from the same area).

  • Students need to pass the courses with a grade of B+ or higher.

  • Breadth courses cannot be waived (these courses must be taken while in the PhD program).

  • Three foundation/breadth requirements need to be completed by the end of year two. All courses need to be completed by the end of year three. Any deviation from this timeline must be approved in advance by the student advisor and the director of the PhD program.

Complete at least 3 of the following:
Formal Foundations

Courses in this group emphasize mathematics and formal reasoning as it applies to foundational questions about computation.

Complete any of the following course(s):
APPPHYS 229

Statistical Mechanics of Learning and Computation

3 Units
CS 154

Introduction to the Theory of Computation

3-4 Units
CS 157

Computational Logic

3 Units
CS 166

Advanced Data Structures

3-4 Units
CS 168

The Modern Algorithmic Toolbox

3-4 Units
CS 205L

Continuous Mathematical Methods with an Emphasis on Machine Learning

3 Units
CS 225

Machine Learning for Discrete Optimization

3 Units
CS 229M

Machine Learning Theory

3 Units
CS 250

Algebraic Error Correcting Codes

3 Units
CS 254

Computational Complexity

3 Units
CS 254B

Computational Complexity II

3 Units
CS 255

Introduction to Cryptography

3 Units
CS 256

Algorithmic Fairness

3 Units
CS 257

Introduction to Automated Reasoning

3 Units
CS 258

Quantum Cryptography

3 Units
CS 259Q

Quantum Computing

3 Units
CS 261

Combinatorial Optimization

3 Units
CS 263

Counting and Sampling

3 Units
CS 264

Beyond Worst-Case Analysis

3 Units
CS 265

Randomized Algorithms and Probabilistic Analysis

3 Units
CS 269I

Incentives in Computer Science

3 Units
CS 328

Foundations of Causal Machine Learning

3 Units
CS 354

Topics in Intractability: Unfulfilled Algorithmic Fantasies

3 Units
CS 355

Advanced Topics in Cryptography

3 Units
CS 357S

Formal Methods for Computer Systems

3 Units
CS 359D

Quantum Complexity Theory

3 Units
CS 369O

Optimization Algorithms

3 Units
EE 276

Information Theory

3 Units
EE 364A

Convex Optimization I

3 Units
EE 364B

Convex Optimization II

3 Units
EE 377

Information Theory and Statistics

3 Units
PHIL 251

Metalogic

4 Units
STATS 311

Information Theory and Statistics

3 Units
STATS 200

Introduction to Theoretical Statistics

3-4 Units
  • CS334a

or
Learning and Modeling

Courses in this group seek to build models of observed phenomena. They emphasize building these models from data, with the goal of predicting, classifying or otherwise structuring observations.

Complete any of the following course(s):
CS 205L

Continuous Mathematical Methods with an Emphasis on Machine Learning

3 Units
CS 221

Artificial Intelligence: Principles and Techniques

3-4 Units
CS 223A

Introduction to Robotics

3 Units
CS 224N

Natural Language Processing with Deep Learning

3-4 Units
CS 224R

Deep Reinforcement Learning

3 Units
CS 224S

Spoken Language Processing

2-4 Units
CS 224U

Natural Language Understanding

3-4 Units
CS 224V

Agentic AI

3-4 Units
CS 224W

Machine Learning with Graphs

3-4 Units
CS 227A

Robot Perception

3-4 Units
CS 227B

General Game Playing

3 Units
CS 228

Probabilistic Graphical Models: Principles and Techniques

3-4 Units
CS 229

Machine Learning

3-4 Units
CS 229B

Machine Learning for Sequence Modeling

3-4 Units
CS 229M

Machine Learning Theory

3 Units
CS 230

Deep Learning

3-4 Units
CS 231A

Computer Vision: From 3D Perception to 3D Reconstruction and Beyond

3-4 Units
CS 231N

Deep Learning for Computer Vision

3-4 Units
CS 233

Geometric and Topological Data Analysis

3 Units
CS 234

Reinforcement Learning

3 Units
CS 236

Deep Generative Models

3 Units
CS 237A

Principles of Robot Autonomy I

3 Units
CS 237B

Principles of Robot Autonomy II

3-4 Units
CS 238

Decision Making under Uncertainty

3-4 Units
CS 239

Advanced Topics in Sequential Decision Making

3-4 Units
CS 248A

Computer Graphics: Rendering, Geometry, and Image Manipulation

3-4 Units
CS 248B

Fundamentals of Computer Graphics: Animation and Simulation

3 Units
CS 271

Algorithmic Foundations for Artificial Intelligence in Healthcare

3-4 Units
CS 272

Introduction to Biomedical Informatics Research Methodology

3 Units
CS 273B

Deep Learning in Genomics and Biomedicine

3 Units
CS 274

Representations and Algorithms for Computational Molecular Biology

1-4 Units
CS 277

Foundation Models for Healthcare

3 Units
CS 279

Computational Biology: Structure and Organization of Biomolecules and Cells

3 Units
CS 281

Ethics of Artificial Intelligence

3-4 Units
CS 326

Topics in Advanced Robotic Manipulation

3-4 Units
CS 329A

Self Improving AI Agents

3 Units
CS 329D

Machine Learning Under Distributional Shifts

3 Units
CS 329H

Machine Learning from Human Preferences

3 Units
CS 329T

Trustworthy Machine Learning: Building and evaluating agentic systems

3 Units
CS 329X

Human Centered NLP

3 Units
CS 330

Deep Multi-task and Meta Learning

3 Units
CS 332

Advanced Survey of Reinforcement Learning

3 Units
CS 333

Algorithms for Interactive Robotics

3-4 Units
CS 336

Language Modeling from Scratch

3-5 Units
CS 348B

Computer Graphics: Image Synthesis Techniques

3-4 Units
CS 348C

Computer Graphics: Animation and Simulation

3 Units
CS 348E

Character Animation: Modeling, Simulation, and Control of Human Motion

3 Units
CS 348I

Computer Graphics in the Era of AI

3-4 Units
CS 348N

Neural Models for 3D Geometry

3 Units
CS 375

Large-Scale Neural Network Modeling for Neuroscience

3 Units
CS 312

Deep Learning Alchemy

3-5 Units
CS 321M

AI Measurement Science

3 Units
CS 329Z

Engineering AI Agents

3 Units
  • CS273A

  • CS371

  • CS373

or
Systems

Courses in this group explore the construction of computing artifacts that meet design constraints or requirements. They emphasize not only what we build but how we build it and the challenges in doing so. Problems in this area often, but not always, include performance (speed, memory, energy), scalability, tradeoffs, complex software, and considering the quality of the artifact itself in addition to its capabilities.

Complete any of the following course(s):
CS 112

Operating systems kernel implementation project

3 Units
CS 140E

Operating systems design and implementation

3-4 Units
CS 143

Compilers

3-4 Units
CS 144

Introduction to Computer Networking

3-4 Units
CS 145

Intro to Modern Data Systems

3-4 Units
CS 149

Parallel Computing

3-4 Units
CS 155

Computer and Network Security

3 Units
CS 212

Operating Systems and Systems Programming

3-5 Units
CS 217

Hardware Accelerators for Machine Learning

3-4 Units
CS 229S

Systems for Machine Learning

3 Units
CS 240

Advanced Topics in Operating Systems

3 Units
CS 240LX

Advanced Systems Laboratory, Accelerated

3 Units
CS 242

Programming Languages

3-4 Units
CS 243

Program Analysis and Optimizations

3-4 Units
CS 244

Advanced Topics in Networking

3-4 Units
CS 244B

Distributed Systems

3 Units
CS 245

Principles of Data-Intensive Systems

3-4 Units
CS 246

Mining Massive Data Sets

3-4 Units
CS 249I

The Modern Internet

3 Units
CS 251

Cryptocurrencies and blockchain technologies

3 Units
CS 348K

Visual Computing Systems

3-4 Units
CS 349D

AI Inference Infrastructure

3 Units
CS 350S

Privacy-Preserving Systems

3 Units
CS 356

Topics in Computer and Network Security

3 Units
EE 180

Digital Systems Architecture

4 Units
EE 282

Computer Systems Architecture

3 Units
  • EE382E

or
People and Society

Courses in this group examine how computing and technology affect and interact with humans and societies, seeking to place artifacts in the context in which they are used. In particular, this area will include courses dealing with computation’s societal impact and HCI courses. 

Complete any of the following course(s):
AMSTUD 145

Silicon Valley

5 Units
BMDS 215

Data Science for Medicine

3 Units
BMDS 223

Deploying and Evaluating Fair AI in Healthcare

2-3 Units
BIOS 244

Applied Artificial Intelligence in Health Care

2 Units
COMM 254

The Politics of Algorithms

4-5 Units
COMM 266

Virtual People

4-5 Units
COMM 286

Media, Technology, and the Body

4-5 Units
COMM 324

Language and Technology

3-5 Units
CS 125

Data: Algorithms, Tools, Policy, and Society

3 Units
CS 147

Introduction to Human-Computer Interaction Design

3-5 Units
CS 148

Introduction to Computer Graphics and Imaging

3-4 Units
CS 152

Trust and Safety

3 Units
CS 181

Computers, Ethics, and Public Policy

4 Units
CS 182

Ethics, Public Policy, and Technological Change

5 Units
CS 194H

User Interface Design Project

3-4 Units
CS 206

Exploring Computational Journalism

3 Units
CS 207

Antidiscrimination Law and Algorithmic Bias

3 Units
CS 222

AI Agents and Simulations

3-4 Units
CS 224C

NLP for Computational Social Science

3 Units
CS 247A

Design for Artificial Intelligence

3-4 Units
CS 247B

Design for Behavior Change

3-4 Units
CS 247G

Design for Play

3-4 Units
CS 247I

Design for Understanding

3-4 Units
CS 247S

Service Design with AI

3-4 Units
CS 256

Algorithmic Fairness

3 Units
CS 269I

Incentives in Computer Science

3 Units
CS 278

Social Computing

3-4 Units
CS 281

Ethics of Artificial Intelligence

3-4 Units
CS 283

Governing Artificial Intelligence: Law, Policy, and Institutions

3 Units
CS 293

Empowering Educators via Language Technology

2-4 Units
CS 323

The AI Awakening: Implications for the Economy and Society

3-4 Units
CS 324H

History of Natural Language Processing

3-4 Units
CS 329H

Machine Learning from Human Preferences

3 Units
CS 329R

Race and Natural Language Processing

3 Units
CS 329T

Trustworthy Machine Learning: Building and evaluating agentic systems

3 Units
CS 329X

Human Centered NLP

3 Units
CS 337

AI-Assisted Care

1-4 Units
CS 347

Human-Computer Interaction: Foundations and Frontiers

3-4 Units
CS 377E

Designing Solutions to Global Grand Challenges

3-4 Units
CS 377G

Designing Serious Games

3-4 Units
CS 377J

Designing Systems for Collaboration, Cooperation, and Collective Action

3 Units
CS 377Q

Designing for Accessibility

3-4 Units
CS 377U

Understanding Users

3-4 Units
CS 384

Seminar on Ethical and Social Issues in Natural Language Processing

3-4 Units
CS 448B

Data Visualization

3-4 Units
CS 470

Music and AI

3-4 Units
DESIGN 255

The Design of Data

3 Units
EARTHSYS 213

Innovating for Climate and Sustainability

3 Units
EDUC 315A

Introduction to CSCL: Computer-Supported Collaborative Learning

3 Units
EDUC 484

Knowledge in the Age of AI: New Epistemic Issues in Education

3 Units
EPI 220

Deploying and Evaluating Fair AI in Healthcare

2-3 Units
HISTORY 244F

New Directions in Gendered Innovations in Science, Medicine, Technology, and Environment

4-5 Units
LAW 4046

Data: Privacy, Property and Security

3 Units
LAW 7073

Antidiscrimination Law and Algorithmic Bias

3 Units
LAW 7102

Regulation, Evaluation, and Governance Lab: Practicum

1-5 Units
LAW 7117

Platform Regulation and the First Amendment

2-3 Units
LAW 7521

Data: Algorithms, Tools, Policy, and Society

2 Units
MS&E 254

The Ethical Analyst

3 Units
POLISCI 150A

Data Science for Politics

3-5 Units
POLISCI 182

Ethics, Public Policy, and Technological Change

5 Units
PUBLPOL 103F

Ethics of Truth in a Post-Truth World

3 Units
PUBLPOL 353A

Science and Technology Policy

5 Units
PUBLPOL 353B

Regulating Emerging Technology: Risks, Opportunities, and Reality

3 Units
STATS 354

Generalization and Causality in Biohealth

1-3 Units
LAW 4052

Governing Artificial Intelligence: Law, Policy, and Institutions

3 Units
  • ANTHRO 132D

  • ENGLISH 184D

  • INTLPOL362

  • LAW 4039

  • LAW 807Z

  • ME 177

  • MS&E 231

  • MS&E 234

Qualifying Examination

Overview

The Qualifying Examination tests a student’s depth of knowledge and familiarity in their area of specialization. Qualifying Exams are generally offered in all areas covered by the written Comprehensive Exam. A student can request a Qualifying Exam in an area not already offered, such as one that cuts across current divisions. The feasibility of such a request is determined on a case-by-case basis by the Ph.D. Program Committee. A student should pass a qualifying exam no later than the end of their third year.

A student may take the Qualifying exams only twice. In some cases, a conditional pass is awarded. The student is credited with the Pass when the designated conditions have been met (such as TAing a particular class, taking a course, or reading additional material in a specific area). If a student fails the Qualifying Exam a second time, the PhD Program Committee is contacted, as this would mean the student is not “making reasonable progress.” This is cause for dismissal by default from the PhD program. The Qualifying Exams are a university requirement and are taken very seriously. Therefore, sufficient time and in-depth preparation must be given to the student’s Quals area to ensure success.

The format of the Qualifying Exams varies from year to year and area to area depending on the faculty member or Quals Chair in charge of each specific exam. Examples are in-class written exams, “take-home” written exams, oral exams, written assignments, and/or a combination of the above. The Quals Chair administers the exams, and the results must be submitted to the Ph.D. Program Officer for the required entry into the university’s Axess (PeopleSoft) and departmental database systems. Passing the Qualifying Exam certifies the student is ready to begin dissertation work in the chosen area. If a student wishes to do dissertation work in an area other than their Qualifying Exam area, the student’s advisor and/or the faculty in the new area will determine whether an additional exam is required.

Reading Committee

Overview

After passing the Qualifying Examination, a student must secure the agreement of a department faculty member to act as the dissertation advisor. Typically, this is the program advisor, but sometimes the dissertation advisor may be in another department. In addition, the student must form a Dissertation Reading Committee composed of the principal dissertation advisor and at least two additional readers.

Reading Committee

The Reading Committee Form opens in a new tab should be signed by all the committee members and submitted to the CS PhD Student Services at phdstudentservices@cs.stanford.edu no later than one year after passing a qualifying exam.

Regulations Concerning Composition of Reading Committee

  • The principal advisor and at least one of the other committee members must be Academic Council members. This is a university regulation.

  • At least two committee members must be CS professors or joint CS professors (academic council members, i.e., Stanford faculty). Courtesy and Adjunct CS professors do not count. This is a departmental requirement.

  • The Reading Committee supervises the dissertation research, advises the student, evaluates the student’s progress, and signs the final draft of the dissertation.

  • These Committee Members also often serve on the student’s Orals Committee because of their research knowledge.

  • Readers, not Academic Council members (limit one), need prior approval from the Graduate Division to serve on your Reading Committee by filing a Petition Form opens in a new tab. 

Thesis Proposal

Overview

The thesis proposal allows students to obtain formative feedback from their reading committee to guide them to a successful, high-quality dissertation. The thesis proposal (a private session only with the student's advisor/co-advisor and reading committee members) should allow time for discussion with the reading committee about the direction of the thesis research.

Thesis Proposal

The student must present an oral thesis proposal and submit the form to their full Reading Committee by the spring quarter of the fourth year. The Thesis Proposal Form opens in a new tab must be filled out, signed, and approved by all the committee members and submitted to the CS PhD Student Services at phdstudentservices@cs.stanford.edu.

The suggested format should include:

  • A description of the research problem and its significance

  • A description of previous work in the area and the “state of the art” before the student’s work

  • A description of preliminary work the student has done on the problem and any research results of that work 

  • An outline of the remaining work to be done and a timeline for accomplishing it

Teaching Requirement

Teaching

Overview

Teaching experience is valuable for CS PhD students. It allows them to demonstrate their knowledge of CS topics, improve their public speaking and presentation skills, and explore future career options in academia.

Teaching Requirement

CS PhD Students are required to complete course assistantships (CA) or teaching fellow (TF) during their academic career for courses in Computer Science numbered 100 or above. This requirement can be completed with either two (2) 50% appointments or four (4) 25% appointments of CAships or TF for a total of 100%.

A CA receives the same salary and tuition benefits as a research assistant (RA). A TF receives a higher salary.

Note: The teaching requirement must be completed by the fourth year. As a matter of policy, a student should satisfy half of the teaching requirement in the first two years of their program. Students are encouraged to complete the teaching requirement early in their stay to eliminate conflicts with later dissertation work. 

Certificate of Distinction in Teaching Award

A graduating PhD student is eligible to receive a certificate of distinction in teaching if the following conditions are met:

  • At least one CS faculty member who has supervised the student as a course assistant nominates the student for this distinction; this nomination is e-mailed to Jay Subramanian.

  • The student should have completed five (5) 50% CAships as a Course Assistant (CA) in CS courses, of which at least one course should be taught as a Teaching Fellow (TF) with primary responsibility for organizing and teaching a course (tutored video does not qualify).

Advancement to Candidacy

Overview

Students are expected to be admitted to candidacy by the end of their second year of doctoral study. To be eligible to apply for candidacy, students must meet the following criteria:

  • Students have permanently aligned with an advisor/s. Aligning with an advisor outside of the CS Department will require a CS co-advisor (courtesy faculty do not count).

  • Have completed three (3) foundation opens in a new tab/breadth requirements; this could be a combination of foundation and breadth courses taken as a current CS PhD student (foundation waivers do not count). For more information, please visit: Foundation & Breadth Requirements opens in a new tab.

  • Have completed at least a three-unit graduate-level course with each of the four instructors who are members of the academic council as a CS PhD student.

Candidacy allows students to qualify for the post-candidacy rate and initiates the five-year timeline to fulfill the remaining PhD requirements.

Oral Exam

Overview

Stanford University requires an Oral Examination for the PhD Program. The department chooses the format of the University Oral Examination. In the Computer Science Department, it is a Defense of the dissertation. The Oral examination format is a public presentation lasting approximately one hour, followed by questions from the examining committee in a private session (a maximum of three hours).

The university oral examination must be completed at least a quarter before the degree is conferred. Orals may be scheduled any time after a substantial portion of the dissertation is complete. The University prefers that orals not be scheduled during the first two weeks of the quarter, finals week, or breaks.

University Oral Examination

The Orals Committee must have at least five members, including one Chair and four voting members. Four of the five must be Academic Council members. The Orals committee will consist of the following:

  • Chair. The chair of the oral committee is typically selected by the student and advisor, usually at the suggestion of the reading committee members. The chair must be an academic council member and may be a professor emeritus.

    • The chair of the examining committee may not have a full or joint appointment in the advisor's or student's department but may have a courtesy appointment in the department.

    • The chair can be from the same Department as any other member(s) of the examination committee. It can be from the student's minor Department, provided that the student's advisor does not have a full or joint appointment in the minor Department.

    • To maintain impartiality, the oral chair may not simultaneously serve on the student's dissertation reading committee. 

  • Readers. The student’s Reading Committee members usually serve on the Orals Committee. Readers who are not Academic Council members (limit 1) need prior approval from the Graduate Division to sit on the Orals Committee by filing a Petition Form opens in a new tab along with the Orals Form. 

  • Advisor. The student’s advisor is a member of the committee.

A student must submit a University Oral Exam Schedule form opens in a new tab and a copy of their dissertation abstract at least two weeks before the proposed Orals date. The PhD student services office will ensure the committee is composed correctly.

Form/s should be submitted as a PDF and emailed to phdstudentservices@cs.stanford.edu. Jay Subramanian, Director of Graduation Admissions and PhD Program, approves all forms on behalf of the Department chair. The CS PhD Student Services Team will collect her signature after you submit your form/s.

Dissertation

Overview

The most important requirement for the PhD degree is the dissertation, which is the culmination of the student's research and hard work.

Dissertation

Review the Dissertation and Theses opens in a new tab website for a comprehensive list of items to prepare and submit for your dissertation.

The Dissertation & Thesis Center opens to submissions on the first day of instruction each quarter. Students must first enroll and have applied to graduate to access the portal.