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Management Science and Engineering
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  • Doctor of Philosophy
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  5. Doctor of Philosophy

Doctor of Philosophy

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

Overview

The PhD degree in MS&E is intended for students primarily interested in a career of research and teaching or high-level technical work in universities, industry, or government.

Requirements

The program requires a minimum of three years of full-time graduate study. Typically, however, students take four to five years after entering the program to complete all PhD requirements. The PhD requires a minimum of 135 units, up to 45 units of which may be transferred from another graduate program. The PhD is organized around the expectation that the students acquire a certain breadth across all department areas and depth in one of them. The current areas are:

  • Computational Social Science

  • Decision and Risk Analysis

  • Operations Research

  • Organizations, Technology, and Entrepreneurship

  • Policy and Strategy

  • Quantitative Finance

Doctoral students are required to take a seminar in autumn quarter specifically designed for first-year students (breadth) and several specified courses in one of the areas of the department (depth). All courses used to satisfy depth requirements must be taken for a letter grade if the letter-graded option is available. Before candidacy, at least three units of work must be taken with each of four Stanford faculty members.

Each student admitted to the PhD program must pass an area qualification procedure. The qualification procedure aims to assess the student’s command of the field and evaluate their potential to complete a high-quality dissertation based on research that must make an original contribution to knowledge in a timely manner.

Finally, the student must complete a PhD dissertation and pass a university oral examination, which is a defense of the dissertation. During the PhD program, students who do not have a master’s degree are strongly encouraged to complete one, either in MS&E or in another Stanford department.

Degree Progress and Student Responsibility

Each doctoral student’s progress is reviewed annually by the MS&E faculty. Typically, this occurs at a faculty meeting at the end of spring quarter, and an appropriate email notification is sent over the summer to the student and their advisor. The student is responsible for initiating each required step in completing the PhD program.

Annual Doctoral Student Degree and Career Progress Meeting (IDP)

Students are expected to meet annually with their advisors to take stock, set goals, and develop an action plan for the coming year. This worksheet opens in a new tab, endorsed by the Committee on Graduate Studies, can help facilitate such conversations. Please complete this form opens in a new tab to let us know when you have completed each year’s discussion.

To maintain good standing in the degree program, first-year students must:

  • Complete 30 units, including the first-year seminar and doctoral courses taught by faculty in their research area

  • Develop relationships with faculty members who can serve as dissertation advisors or reading committee members. A faculty member is more likely to accept the responsibility of supervising the research of a student they know reasonably well than a student whose abilities, initiative, and originality the faculty member knows less well. It is recommended that students participate in research rotations with MS&E and related faculty to facilitate the development of these relationships.

To maintain good standing in the degree program, second-year students must:

  • Complete at least two one-quarter research rotations or tutorials, or one two-quarter research rotation, tutorial, or research paper or project, continuing to develop relationships with faculty members who might serve as dissertation advisors or reading committee members

  • Pass an area-qualifying exam or defense of the research

  • Submit a candidacy form signed by at least one MS&E faculty member with whom they have or will complete research rotations, tutorials, or papers or projects, and listing the course requirements agreed upon by both the student and the program advisor

  • Complete 30 units, including most, if not all, of the required courses listed on the candidacy form

  • Be advanced to candidacy by the faculty

To maintain good standing in the degree program, third-year students must:

  • Submit a progress form listing the dissertation topic and signed by the dissertation advisor (if the dissertation advisor is not an MS&E faculty member, the form must also be signed by an MS&E faculty member who agrees to be on the student’s reading committee, as well as the student’s point of contact within the department)

  • Complete 30 units, including any remaining depth courses.

To maintain good standing in the degree program, fourth-year students must:

  • Select a reading committee (a dissertation advisor and two readers) with at least one member from the student’s major department, and submit the reading committee form signed by each member of the reading committee

  • Make satisfactory progress on their dissertation as determined by their dissertation advisor

  • Complete 30 dissertation units (if the student has not transferred any previous graduate units to Stanford)

To maintain good standing in the degree program beyond the fourth year, students must make satisfactory progress on their dissertation as determined by their dissertation advisor and approved by the faculty. Indeed, the dissertation advisor will have to present the case to (and seek approval for the student’s good standing from) the faculty in the annual faculty meeting for student review. It should be noted that each student inherently has to pass the oral examination (see below) and submit their dissertation before their candidacy expires.

Aligning with an advisor is an essential milestone in the PhD program and is required for making satisfactory degree progress and maintaining good standing. PhD students who do not align with an advisor by the end of the second year at Stanford are subject to dismissal from the program. PhD students may need to change advisors after initial alignment due to unexpected circumstances. Since an advisor is necessary for continued funding and good standing, please consult the Director of Student and Academic Services or Director of Graduate Studies as early as possible to ensure proper coordination and uninterrupted support. Additional guidance for students considering an advisor change is available in this resource opens in a new tab. If a student needs to change advisors, the Department requires the student to actively seek and find a new advisor within one quarter, to satisfy minimum progress requirements of the Department and the University and continue enrolling. If the student is unable to secure a new advisor by the end of the quarter, the Department will initiate procedures for dismissal per the “Guidelines for Dismissal of Graduate Students for Academic or Professional Reasons” in the Stanford Bulletin. 

Any exceptional cases for a student to remain in good standing based on extenuating circumstances must be presented to and approved by the whole faculty.

Program Requirements

Breadth Requirement

All first-year students must attend and participate in MS&E 302 Fundamental Concepts in Management Science and Engineering, which meets in the autumn quarter.

Each course session is devoted to a specific MS&E PhD research area. At a given session, several advanced PhD students in that area make carefully prepared presentations designed for first-year doctoral students regardless of area. The presentations are devoted to:

  • Illuminating how people in the area being explored that day think about and approach problems

  • Illustrating what can and cannot be done when addressing problems by deploying the knowledge, perspectives, and skills acquired by those who specialize in the area in question

Faculty in the focal area of the week comment on the weekly student presentations. The rest of the session is devoted to questions posed and comments made by first-year PhD students.

During the last two weeks of the quarter, groups of first-year students present how they would approach a problem drawing on two or more perspectives to which they have been exposed earlier in the class.

Attendance is mandatory, and performance is assessed based on the quality of the student’s presentations and class participation.

Complete all of the following course(s):
MS&E 302

Fundamental Concepts in Management Science and Engineering

1 Units

Path Requirements

Complete any of the following requirements:
Computational Social Science

Complete four courses, with at least one from each CSS sub-area.

Complete all of the following requirements:
CSS Statistics
Complete at least 1 of the following courses:
MS&E 226

Fundamentals of Data Science: Prediction, Inference, Causality

3 Units
MS&E 228

Applied Causal Inference with Machine Learning and AI

3 Units
MS&E 328

Foundations of Causal Machine Learning

3 Units
STATS 202

Statistical Learning and Data Science

3 Units
STATS 203

Regression Models and Analysis of Variance

3 Units
STATS 305A

Applied Statistics I

3 Units
STATS 315A

Modern Applied Statistics: Learning

3 Units
STATS 315B

Modern Applied Statistics: Learning II

3 Units
STATS 361

Causal Inference

3 Units
and
CSS Computation
Complete at least 1 of the following courses:
CS 224N

Natural Language Processing with Deep Learning

3-4 Units
CS 224S

Spoken Language Processing

2-4 Units
CS 224W

Machine Learning with Graphs

3-4 Units
CS 229

Machine Learning

3-4 Units
CS 246

Mining Massive Data Sets

3-4 Units
MS&E 230

Market Design for Engineers

3 Units
MS&E 235A

Markov Decision Processes

3 Units
MS&E 235B

Reinforcement Learning: Behaviors and Applications

3 Units
MS&E 236

Machine Learning for Discrete Optimization

3 Units
MS&E 239

Market Design in Action

3 Units
MS&E 312

Optimization Algorithms

3 Units
MS&E 315

Combinatorial Optimization

3 Units
STATS 290

Computing for Data Science

3 Units
and
CSS Social Data
Complete at least 1 of the following courses:
ECON 291

Social and Economic Networks

4 Units
MS&E 232

Introduction to Game Theory

3 Units
or
MS&E 232H

Introduction to Game Theory (Accelerated)

3 Units
MS&E 233

Game Theory, Data Science and AI

3 Units
MS&E 241

Economic Analysis

3-4 Units
MS&E 270

Strategy in Technology-Based Companies

3 Units
MS&E 280

Organizational Behavior: Evidence in Action

3 Units
MS&E 284

The Future of Work: What Will it Mean to Build AI-Augmented Organizations?

3 Units
MS&E 325

Diffusion, Control and Optimal Transport

3 Units
MS&E 334

Topics in Social Data

3 Units
MS&E 336

Computational Social Choice

3 Units
MS&E 338

Aligning Superintelligence

3 Units
MS&E 365

Topics in Market Design

3 Units
MS&E 366

Market Design and Resource Allocation in Non-Profit Settings

3 Units
MS&E 376

Strategy Doctoral Research Seminar

1-3 Units
SOC 369

Social Network Methods

3-5 Units
and
CSS Elective

Complete at least one additional course from any of the three CS sub areas.

or
Decision and Risk Analysis
Complete all of the following requirements:
D&RA Required Courses
Complete all of the following course(s):
EE 263

Matrix Methods: Singular Value Decomposition

3 Units
MS&E 211

Introduction to Optimization

3-4 Units
or
MS&E 211DS

Introduction to Optimization: Data Science

3-4 Units
or
MS&E 211X

Introduction to Optimization (Accelerated)

3-4 Units
or
MS&E 311

Optimization

3 Units
or
MS&E 312

Optimization Algorithms

3 Units
MS&E 220

Probabilistic Analysis

3 Units
MS&E 221

Stochastic Modeling

3 Units
or
STATS 217

Introduction to Stochastic Processes I

3 Units
MS&E 223

Stochastic Simulation and Monte Carlo Methods

3 Units
MS&E 241

Economic Analysis

3-4 Units
MS&E 250A

Reliability and Resilience of Engineered Systems

3 Units
MS&E 250B

Project Course in Engineering Risk Analysis

3 Units
MS&E 252

Foundations of Decision Analysis

3 Units
MS&E 355

Influence Diagrams and Probabilistics Networks

3 Units

Students are expected to have previously completed computer programming, multivariable calculus and linear algebra, and matrix methods or will need to take those courses in addition to the degree requirements.

or
Operations Research
Complete all of the following requirements:
OR Optimization Core
Complete any of the following course(s):
MS&E 310

Linear Programming

3 Units
MS&E 311

Optimization

3 Units
MS&E 312

Optimization Algorithms

3 Units
and
OR Stochastics Core
Complete any of the following course(s):
MS&E 321

Stochastic Systems

3 Units
MS&E 322

Stochastic Calculus and Control

3 Units
and
OR Core Elective
Complete any of the following course(s):
MS&E 310

Linear Programming

3 Units
MS&E 311

Optimization

3 Units
MS&E 312

Optimization Algorithms

3 Units
MS&E 315

Combinatorial Optimization

3 Units
MS&E 319

Efficient Generative Models

3 Units
MS&E 321

Stochastic Systems

3 Units
MS&E 322

Stochastic Calculus and Control

3 Units
MS&E 323

Stochastic Simulation

3 Units
MS&E 324

Stochastic Methods in Engineering

3 Units
MS&E 325

Diffusion, Control and Optimal Transport

3 Units
MS&E 326

Advanced Topics in Applied Data Science

3 Units
MS&E 328

Foundations of Causal Machine Learning

3 Units
and
OR Elective Courses
Complete at least 3 of the following courses:
MS&E 310

Linear Programming

3 Units
MS&E 311

Optimization

3 Units
MS&E 312

Optimization Algorithms

3 Units
MS&E 315

Combinatorial Optimization

3 Units
MS&E 318

Safe and Constrained AI

3 Units
MS&E 319

Efficient Generative Models

3 Units
MS&E 321

Stochastic Systems

3 Units
MS&E 322

Stochastic Calculus and Control

3 Units
MS&E 323

Stochastic Simulation

3 Units
MS&E 324

Stochastic Methods in Engineering

3 Units
MS&E 325

Diffusion, Control and Optimal Transport

3 Units
MS&E 326

Advanced Topics in Applied Data Science

3 Units
MS&E 328

Foundations of Causal Machine Learning

3 Units
MS&E 331

AI for Algorithmic Reasoning and Optimization

3 Units
MS&E 333

AI Application Lab

3 Units
MS&E 335

Queueing and Scheduling in Processing Networks

3 Units
MS&E 336

Computational Social Choice

3 Units
MS&E 337

Large Networks and Graph Limits

3 Units
MS&E 338

Aligning Superintelligence

3 Units
MS&E 342

Stochastic Systems and Learning Theory with Applications in Finance

3 Units
MS&E 349

Financial Statistics

3 Units
MS&E 365

Topics in Market Design

3 Units
MS&E 366

Market Design and Resource Allocation in Non-Profit Settings

3 Units

Up to two elective courses may be 200-level MS&E, CS, EE, or STATS non-seminar courses if approved by the student’s academic advisor or research advisor.

At least six core or elective courses required, with at least four listed or cross-listed in MS&E.

or
Organizations, Technology and Entrepreneurship
Complete all of the following requirements:
OTE Foundation in Organizational Behavior
Complete at least 4 of the following courses:
MS&E 370

Current Topics in Strategy, Innovation and Entrepreneurship

1-3 Units
MS&E 371

Innovation and Strategic Change

1-3 Units
MS&E 372

Entrepreneurship Doctoral Research Seminar

3 Units
MS&E 376

Strategy Doctoral Research Seminar

1-3 Units
MS&E 379

Social Data Analysis

3 Units
MS&E 382

Meso-Organizational Theory

3 Units
MS&E 383

Interviewing

3 Units
MS&E 384

Groups and Teams

3 Units
MS&E 385

Doctoral Seminar in Race and Ethnicity

3 Units
MS&E 386

Doctoral Research Seminar on Technology & Organizations

3 Units
MS&E 388

Contemporary Themes in Work and Organization Studies

3 Units
and
OTE Statistics and Research Methods
Complete at least 3 of the following courses:
OB 670

Designing Social Research

3 Units
OB 678

The Design and Process of Experimental Research

1 Units
POLISCI 450A

Political Methodology I: Regression

3-5 Units
POLISCI 450B

Political Methodology II: Causal Inference

5 Units
POLISCI 450D

Political Methodology IV: Advanced Topics

3-5 Units
POLISCI 450C

Political Methodology III: Model-Based Inference

3-5 Units
PSYCH 252

Statistical Methods for Behavioral and Social Sciences

5 Units
SOC 381

Statistics for an open science: Foundation

4-5 Units
SOC 382

Statistics for an Open Science: Explanation

4-5 Units
SOC 383

Statistics for an Open Science: Modeling

5 Units
SOC 384

Advanced Regression Analysis

3-5 Units

Students may petition other relevant statistics and research methods courses.

or
Policy and Strategy
Complete all of the following requirements:
P&S Foundation
Complete any of the following course(s):
MS&E 376

Strategy Doctoral Research Seminar

1-3 Units
MS&E 390

Doctoral Research Seminar in Health Systems Modeling

1-3 Units
and
P&S Statistics and Research Methods
Complete at least 3 of the following courses:
ECON 270

Intermediate Econometrics I

2-5 Units
ECON 271

Intermediate Econometrics II

5 Units
EE 364A

Convex Optimization I

3 Units
EPI 225

Introduction to Epidemiologic and Clinical Research Methods

3 Units
EPI 226

Intermediate Epidemiologic and Clinical Research Methods

3 Units
EPI 227

Advanced Epidemiologic Methods

3 Units
EPI 262

Intermediate Biostatistics: Regression, Prediction, Survival Analysis

3 Units
MGTECON 603

Econometric Methods I

4 Units
MGTECON 604

Econometric Methods II

4 Units
MGTECON 605

Econometric Methods III

3 Units
MS&E 311

Optimization

3 Units
MS&E 312

Optimization Algorithms

3 Units
MS&E 315

Combinatorial Optimization

3 Units
MS&E 321

Stochastic Systems

3 Units
MS&E 322

Stochastic Calculus and Control

3 Units
MS&E 324

Stochastic Methods in Engineering

3 Units
MS&E 328

Foundations of Causal Machine Learning

3 Units
POLISCI 450A

Political Methodology I: Regression

3-5 Units
POLISCI 450B

Political Methodology II: Causal Inference

5 Units
POLISCI 450C

Political Methodology III: Model-Based Inference

3-5 Units
POLISCI 450D

Political Methodology IV: Advanced Topics

3-5 Units
SOC 369

Social Network Methods

3-5 Units
SOC 381

Statistics for an open science: Foundation

4-5 Units
SOC 382

Statistics for an Open Science: Explanation

4-5 Units
SOC 383

Statistics for an Open Science: Modeling

5 Units
SOC 384

Advanced Regression Analysis

3-5 Units
and
P&S Disciplinary Depth
Complete at least 4 of the following courses:
BMDS 215

Data Science for Medicine

3 Units
CS 277

Foundation Models for Healthcare

3 Units
CS 337

AI-Assisted Care

1-4 Units
HRP 263

Advanced Decision Science Methods and Modeling in Health

3 Units
HRP 392

Introduction to Cost-Effectiveness Analysis: Evaluating Benefits and Costs of Health Interventions

4-5 Units
MS&E 263

Healthcare Operations Management

3 Units
MS&E 292

Health Policy Modeling

3 Units
MS&E 370

Current Topics in Strategy, Innovation and Entrepreneurship

1-3 Units
MS&E 371

Innovation and Strategic Change

1-3 Units
MS&E 372

Entrepreneurship Doctoral Research Seminar

3 Units
MS&E 376

Strategy Doctoral Research Seminar

1-3 Units
MS&E 379

Social Data Analysis

3 Units
MS&E 463

Healthcare Systems Design

3-4 Units

Substitutions may be approved by the P&S faculty.

or
Quantitative Finance
Complete all of the following requirements:
QF Optimization and Stochastics Core
Complete all of the following course(s):
MS&E 211

Introduction to Optimization

3-4 Units
or
MS&E 211DS

Introduction to Optimization: Data Science

3-4 Units
or
MS&E 211X

Introduction to Optimization (Accelerated)

3-4 Units
or
MS&E 310

Linear Programming

3 Units
or
MS&E 311

Optimization

3 Units
or
MS&E 312

Optimization Algorithms

3 Units
MS&E 321

Stochastic Systems

3 Units
or
MS&E 322

Stochastic Calculus and Control

3 Units
or
MS&E 324

Stochastic Methods in Engineering

3 Units
and
QF Statistics Core
Complete any of the following course(s):
STATS 200

Introduction to Theoretical Statistics

3-4 Units
STATS 305A

Applied Statistics I

3 Units
STATS 315A

Modern Applied Statistics: Learning

3 Units
and
QF Numerical Methods Core
Complete any of the following course(s):
CME 200

Linear Algebra with Application to Engineering Computations

3 Units
CME 206

Introduction to Numerical Methods for Engineering

3 Units
MS&E 223

Stochastic Simulation and Monte Carlo Methods

3 Units
MS&E 319

Efficient Generative Models

3 Units
and
QF Electives
Complete at least 3 of the following courses:
MS&E 242

Machine Learning for Algorithmic Trading

3 Units
MS&E 245A

Investment Science

4 Units
MS&E 245B

Advanced Investment Science

3 Units
MS&E 246

Financial Risk Analytics

3 Units
MS&E 247

Decentralized Finance & Blockchain: Innovation, Applications, and Entrepreneurship

3 Units
MS&E 315

Combinatorial Optimization

3 Units
MS&E 325

Diffusion, Control and Optimal Transport

3 Units
MS&E 334

Topics in Social Data

3 Units
MS&E 338

Aligning Superintelligence

3 Units
MS&E 342

Stochastic Systems and Learning Theory with Applications in Finance

3 Units
MS&E 347

Advanced Topics in Blockchain & DeFi: Research, Market Design, and Microstructure

3 Units
MS&E 349

Financial Statistics

3 Units
ECON 236

Financial Economics I

3-5 Units
ECON 237

Heterogeneity in Macroeconomics

4 Units
ECON 273

Advanced Econometrics I

3-5 Units
ECON 274

Advanced Econometrics II

3-5 Units
FINANCE 620

Financial Markets I

3 Units
FINANCE 622

Dynamic Asset Pricing Theory

3 Units
FINANCE 625

Empirical Asset Pricing

3 Units
MATH 238

Mathematical Finance

3 Units
STATS 300A

Theory of Statistics I

3 Units
STATS 310A

Theory of Probability I

3 Units
STATS 315B

Modern Applied Statistics: Learning II

3 Units

Other MS&E, Economics, Finance, Scientific Computing, or Statistics courses at the MS&E 300-level (or comparable in other departments) may be chosen after consulting with the dissertation advisor.

Complete the course requirements and qualification procedure for one of the six paths. Please consult with your MS&E program advisor if you would like to request waivers or substitutions based upon your previous coursework and intended dissertation topic.

Advancement to Candidacy

Qualification Procedure Requirements

The qualification procedure is based on depth in an area of the student’s choice and preparation for dissertation research. The qualification process must be completed by the end of May of the student’s second year of graduate study in the department.

The PhD qualification requirements comprise these elements:

  1. Courses and GPA: Students must complete the depth requirements of one of the areas of the MS&E department. (The PhD area course requirements are below). If the advisor and/or area approves, one or more courses may be completed after advancement to candidacy. All courses used to satisfy depth requirements must be taken for a letter grade if the letter-graded option is available. Course substitutions may be approved by the doctoral program advisor or the MS&E dissertation advisor on the candidacy form. A student must maintain a GPA of at least 3.4 in all courses taken by the student within the department. The GPA is computed based on the nominal number of units for which each course is offered.

  2. Research Paper(s) or Project(s): Students must complete at least one research paper or project over a minimum of two quarters. The student chooses the faculty advisor(s) with the faculty member’s consent.  There must be at least one MS&E faculty member advising and evaluating the research paper(s) or project(s).

  3. Area Qualification: During the second year, a student must pass an examination in one of the areas of the MS&E department or defense of the research. The student chooses the area/program in which to take the examination. This area examination is written, oral, or both at the area faculty’s discretion. There must be a minimum of two faculty evaluators, at least one of whom is an MS&E faculty member, and at least one of whom is not a primary or co-advisor.

The qualification decision is based on the student’s coursework and grade point average (GPA), on the research paper(s) or project(s) prepared by the student with close guidance from the faculty, the student’s performance in an area examination or defense of the research, a commitment or strong interest from at least one faculty member in becoming the dissertation advisor, and an overall assessment by the faculty of the student’s ability to conduct high-quality research. Considering this evidence, the department faculty vote on advancing the student to candidacy in the department at large.

Oral Exam

Oral Exam

As administered in this department, the university oral examination is a defense of the dissertation; however, the candidate should be prepared to answer any question raised by any members of the Academic Council who choose to be present. The examining committee consists of the three members of the reading committee as well as a fourth faculty member and an orals chair. The chair must be an Academic Council member and may not be affiliated with either the Department of Management Science and Engineering or any department where the student’s advisor has a regular appointment; emeriti professors are eligible to serve as an orals chair. The student’s advisor is responsible for finding an appropriate orals chair. The university oral examination may be scheduled after the dissertation reading committee has given tentative approval to the dissertation.

The student must be enrolled in the quarter of their oral examination. Students should schedule three hours for the oral examination, which usually consists of a 45-minute public presentation followed by closed-session questioning of the examinee by the committee and committee deliberation. The student must reserve a room and meet with the Director of Student and Academic Services to complete the oral examination schedule and pick up other paperwork. This paperwork and an abstract must be delivered to the orals chair at least one week before the oral examination.

Dissertation

Dissertation

Students must complete a PhD dissertation and pass a university oral examination, which is a defense of the dissertation.

Information for rules governing the dissertation format, fees, forms, and deadlines can be found on the Dissertations and Theses opens in a new tab website.