General

Program Title
Management Science and Engineering (PhD)
Degree Type
PHD - Doctor of Philosophy
Undergraduate/Graduate
Graduate
Department(s)
Mgmt Sci & Engineering
Program Overview

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

  • Computational Social Science

  • Decision and Risk Analysis

  • Energy and Environment

  • Health Systems Modeling and Policy

  • National Security Policy

  • Operations Research

  • Organizations

  • Quantitative Finance

  • Strategy, Innovation, and Entrepreneurship

Doctoral students are required to take course, our breadth requirement, and a number of specified courses in one of the areas of the department. All courses used to satisfy depth requirements must be taken for a letter grade, if the letter graded option is available. Prior to candidacy, at least 3 units of work must be taken with each of four Stanford faculty members.

Each student admitted to the Ph.D. program must pass an area qualification procedure. The purpose of the qualification procedure is to assess the student’s command of the field and to evaluate his or her potential to complete a high-quality dissertation, based on research which must make an original contribution to knowledge, in a timely manner.

Finally, the student must complete a Ph.D. dissertation, and pass a University oral examination, which is a defense of the dissertation. During the course of the Ph.D. 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.

Breadth Requirement

All first year students are required to attend and participate in course Fundamental Concepts in Management Science and Engineering, which meets in the Autumn Quarter.

Each course session is devoted to a specific MS&E Ph.D. research area. At a given session several advanced Ph.D. students in that area make carefully prepared presentations designed for first-year doctoral students regardless of area. The presentations are devoted to: (a) illuminating how people in the area being explored that day think about and approach problems, and (b) 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 the first year Ph.D. students.

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

Attendance is mandatory and performance is assessed on the basis of the quality of the students’ presentations and class participation

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 the month of May of the student’s second year of graduate study in the department.

The Ph.D. 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 Ph.D. area course requirements are below). 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 adviser or the MS&E dissertation adviser on the candidacy form or on a request for graduate course waiver/substitution form. A student must maintain a GPA of at least 3.4 in the set of all courses taken by the student within the department. The GPA is computed on the basis of the nominal number of units for which each course is offered.

  2. Paper(s): A student may choose between two options. The first option involves one paper supervised by a primary faculty adviser and a second faculty reader. This paper should be written in two quarters. The second option involves two shorter sequential tutorials, with two different faculty advisers. Each tutorial should be completed in one quarter. In both options, the student chooses the faculty adviser(s)/reader with the faculty members’ consent. There must be two faculty members, at least one of whom must be an MS&E faculty member, supervising and evaluating this requirement for advancement to candidacy. The paper/tutorials must be completed before the Spring Quarter of the student’s second year of graduate study in the department if the student's qualifying exam is during the Spring Quarter, and before the end of May of that year otherwise.

  3. Area Qualification: In addition, during the second year, a student must pass an examination in one of the areas of the MS&E department, or defense of the written paper(s). The student chooses the area/program in which to take the examination. This area examination is written, oral, or both, at the discretion of the area faculty administering the exam. Most areas offer the qualifying exam only once per year, which may be early in the second year.

The qualification decision is based on the student’s course work and grade point average (GPA), on the one or two preliminary papers prepared by the student with close guidance from two faculty members, at least one of whom must be an MS&E faculty member, the student’s performance in an area examination or defense of the written paper(s), 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.

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 adviser. It shall be the responsibility of the student to initiate each required step in completing the Ph.D. program.

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

  1. complete 30 units, including course and doctoral courses taught by faculty in their research area;

  2. develop relationships with faculty members who can potentially serve as dissertation adviser or reading committee member. A faculty member is more likely to accept the responsibility of supervising the research of a student whom he or she knows fairly 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:

  1. 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, and listing the course requirements agreed upon by both the student and the program adviser;

  2. complete at least two one-quarter research rotations or tutorials, or one two-quarter research rotation, tutorial, or research paper, continuing to develop relationships with faculty members who might serve as dissertation adviser or reading committee member;

  3. complete 30 units, including most, if not all, of the required courses listed on the candidacy form;

  4. pass an area qualifying exam, or defense of the written paper(s);

  5. be advanced to candidacy by the faculty.

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

  1. submit a progress form listing the dissertation topic and signed by the dissertation adviser (if the dissertation adviser 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);

  2. complete 30 units, including any remaining depth courses.

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

  1. select a reading committee (a dissertation adviser and two readers) with at least one member from the student's major department, and submit the reading committee form signed by each member on the reading committee;

  2. make satisfactory progress on their dissertation as determined by their dissertation adviser;

  3. if the student has not transferred any previous graduate units to Stanford, complete 30 dissertation units.

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 adviser and approved by the faculty. Indeed, the dissertation adviser will have to present the case to (and seek approval for good standing of the student 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.

Additionally, to remain in good standing, and to remain eligible for funding, students must perform well in all assistantship positions.

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

Oral Examination

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 nor any department in which the student's adviser has a regular appointment; emeriti professors are eligible to serve as an orals chair. It is the responsibility of the student's adviser to find 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 needs to reserve a room, and meet with the student services manager to complete the oral examination schedule and pick up other paper work. This paperwork, along with an abstract, needs to be delivered to the orals chair at least one week prior to the oral examination.

Program Learning Outcomes

The Ph.D. is conferred upon candidates who have demonstrated substantial scholarship and the ability to conduct independent research. Through course work and guided research, the program prepares students to make original contributions in Management Science and Engineering and related fields.

External Credit Policies
-

Degree Requirements

Area Requirements

Computational Social Science

The Computational Social Science track teaches students how to apply rigorous statistical and computational methods to address problems in economics, sociology, political science and beyond.  The core course work covers fundamental statistical concepts, large-scale computation, and network analysis. Through electives, students can explore topics such as experimental design, algorithmic economics, and machine learning.

Computational Social Science Qualifying Procedure: The student does two quarter-length tutorials with CSS faculty. At the end of these tutorials, the student must make a 45-minute presentation of one of their tutorials to a committee of three CSS faculty members. The student can do both tutorials with the same faculty member, in which case the presentation can be of the two tutorials together, and another committee member must be kept informed of the student’s progress on a regular basis during the two quarters. The presentation should take place in the Spring Quarter of the student's second year, or earlier. The presentation must include original research or promising directions towards original research.  During this presentation, the student must also provide the name of their chosen focus area, and the list of courses that the student has completed and intends to complete in the core as well as in the chosen focus area. The committee then makes a recommendation to the CSS area and the MS&E department regarding qualification of the student for the Ph.D. program in CSS.

Course List

Select four courses, with at least one from each of the three areas below.

Statistics (select at least one):

course

Fundamentals of Data Science: Prediction, Inference, Causality

course

Topics in Causal Inference

course

Data Mining and Analysis

course

Introduction to Regression Models and Analysis of Variance

course

Applied Statistics I

course

Modern Applied Statistics: Learning

course

Modern Applied Statistics: Data Mining

Computation (select at least one):

course

Market Design for Engineers

course

Introduction to Computational Social Science

course

Natural Language Processing with Deep Learning

course

Machine Learning

course

Mining Massive Data Sets

course

Computing for Data Science

Social Data (select at least one):

course

Introduction to Game Theory

course

Introduction to Game Theory

course

Data Privacy and Ethics

course

Network Structure and Epidemics

course

Economic Analysis

course

Strategy in Technology-Based Companies

course

Organizational Behavior: Evidence in Action

course

Designing Modern Work Organizations

course

Topics in Social Data

course

Machine Learning with Graphs

course

Social and Economic Networks

course

Social Network Methods

Recommended:

course

Market Design and Resource Allocation in Non-Profit Settings

course

Big Data and Causal Inference

course

From Languages to Information

course

Introduction to Human-Computer Interaction Design

course

Spoken Language Processing

course

Data Visualization

course

Microeconomics I For Non-Economics PhDs students

course

Organizational Analysis

course

Programming for Linguists

course

Causal Inference for Social Science

course

Machine Learning with Application to Text as Data

course

Classic and contemporary social psychology research

course

Wise Interventions

course

Social Psychology and Social Change

course

Economic Sociology

course

Social Movements and Collective Action

course

Interpersonal Relations

course

Relational Sociology

course

The Social Regulation of Markets

course

Classics of Modern Social Theory

course

Applications of Causal Inference Methods

course

Design of Experiments

course

Massive Computational Experiments, Painlessly

Decision Analysis and Risk Analysis

The DA&RA qualification process is intended to be completed by the end of the second year, and consists of 

  • research experience, either in the form of a "second year paper" with the student's advisor and a second reader, or two separate research tutorials including one with the student's advisor; and

  • an oral exam on fundamentals of probability, decision analysis, and risk analysis.

Course List

Prerequisites:

course

Programming Methodology

course

Vector Calculus for Engineers

course

Introduction to Matrix Methods (formerly CME 103)

Required:

course

Dynamic Systems

or course

Introduction to Linear Dynamical Systems

course

Introduction to Optimization

or course

Introduction to Optimization (Accelerated)

or course

Introduction to Optimization Theory

or course

Optimization

course

Probabilistic Analysis

course

Stochastic Modeling

or course

Introduction to Stochastic Processes I

course

Simulation

course

Economic Analysis

course

Engineering Risk Analysis

course

Project Course in Engineering Risk Analysis

course

Introduction to Stochastic Control with Applications

or course

Dynamic Programming and Stochastic Control

course

Decision Analysis I: Foundations of Decision Analysis

course

Decision Analysis II: Professional Decision Analysis

course

Decision Analysis III: Frontiers of Decision Analysis

course

Influence Diagrams and Probabilistics Networks

Recommended:

course

Investment Science

course

The Ethical Analyst

course

Strategy in Technology-Based Companies

course

Organizational Behavior: Evidence in Action

course

Stochastic Systems

or course

Introduction to Stochastic Processes II

course

Probabilistic Graphical Models: Principles and Techniques

course

Modeling Biomedical Systems

course

Game Theory and Economic Applications

course

Multiperson Decision Theory

course

Introduction to Statistical Inference

or course

Data Mining and Analysis

or course

Intermediate Econometrics II

Quantitative Finance

The finance area focuses on the quantitative and statistical study of financial risks, institutions, markets, and technology.  Students take courses in probability, statistics, optimization, finance, economics, and computational mathematics as well as a variety of other courses.  Recent dissertation topics include studies of machine learning methods for risk management; systemic financial risk; algorithmic trading; optimal order execution; large-scale portfolio optimization; mortgage markets; and statistical testing of financial models.  Ph.D. students in the area typically are affiliated with the Advanced Financial Technology Laboratory (AFTLab).

Students should discuss their course schedule with their dissertation advisers. Other courses in MS&E, Economics, Finance, Scientific Computing, or Statistics at the MS&E 300-level (or comparable in other departments) may be chosen after consulting with the dissertation adviser.

Quantitative Finance Qualifying Procedure: The student does two quarter-length tutorials with QF faculty. At the end of these tutorials, the student must make a 45-minute presentation of one of their tutorials to a committee of two QF faculty members. The student can do both tutorials with the same faculty member, in which case the presentation can be of the two tutorials together, and another committee member must be kept informed of the student’s progress on a regular basis during the two quarters. The presentation should take place in the Spring Quarter of the student's second year, or earlier. The presentation must include original research or promising directions towards original research. During this presentation, the student must also provide the name of their chosen focus area, and the list of courses that the student has completed and intends to complete in the core as well as in the chosen focus area. The committee then makes a recommendation to the QF area and the MS&E department regarding qualification of the student for the Ph.D. program in QF.

Course List

Required Core Courses (4 courses in total)

Optimization Core (at least one of the following):

course

Introduction to Optimization

course

Introduction to Optimization (Accelerated)

course

Linear Programming

course

Optimization

course

Optimization Algorithms

Stochastics Core (at least one of the following):

course

Stochastic Systems

course

Stochastic Calculus and Control

course

Stochastic Methods in Engineering

Statistics Core (at least one of the following)

course

Introduction to Statistical Inference

course

Applied Statistics I

course

Modern Applied Statistics: Learning

Numerical Methods Core (at least one of the following)

course

Simulation

course

Linear Algebra with Application to Engineering Computations

course

Introduction to Numerical Methods for Engineering

course

Approximation Algorithms

Electives (at least three of the following)

course

Investment Science

course

Advanced Investment Science

course

Financial Risk Analytics

course

Discrete Mathematics

course

Optimal Transport in Operations Research, Statistics, and Economics

course

Topics in Causal Inference

course

Topics in Social Data

course

Reinforcement Learning: Frontiers

course

Credit Risk: Modeling and Management

course

Optimization of Uncertainty and Applications in Finance

course

Financial Statistics

course

Financial Economics I

course

Financial Economics II

course

Advanced Econometrics I

course

Advanced Econometrics II

course

Financial Market I

course

Dynamic Asset Pricing Theory

course

Empirical Asset Pricing

course

Mathematical Finance

course

Statistical Methods in Finance

course

Risk Analytics and Management in Finance and Insurance

course

Theory of Statistics I

course

Theory of Probability I

course

Modern Applied Statistics: Data Mining

Energy and Environment Policy (see Policy and Strategy)

Health Policy (see Policy and Strategy)

National Security Policy (see Decision and Risk Analysis)

Operations Research

The Operations Research (OR) Qualifying Procedure is designed to ensure students have a common set of technical skills, particularly in optimization and stochastics, and can effectively perform research in a coherent area of specialization. The requirements are structured so students can become involved with research and enjoy the benefits of a broad operations research group early in their Ph.D.

Students intending to qualify in the operations research track will be assigned an initial academic (program) advisor with the option to switch advisors (e.g. if the academic advisor is not a good fit, or once a student has found a research advisor (formally, MS&E dissertation advisor) who is not their academic advisor. Academic advisors meet at least once a quarter with advisees to provide guidance on course selection and research/rotation opportunities, and ensure the student is on track to completing the qualification requirements. Further, before a student advances to candidacy, by the end of each quarter the student will update a form outlining a plan for completing the OR track requirements and aligning with a PhD advisor. This form is to be discussed with the academic advisor each quarter. Students should identify their PhD research advisor on the form before the completion of the oral qualification exam.

Students following standard OR track requirements will likely advance to candidacy in the winter or spring quarter of their second year. The operations research qualification process includes the following key components:

  • Research Rotations (due end of fall quarter of student's second year): a student must complete two research rotations with two different faculty, at least one who is affiliated with MS&E (either by appointment or courtesy). Such rotations should consist of regular meetings with a faculty member to discuss research.

  • Technical Evaluation (due end of the winter quarter of a student's second year): the student must obtain an A- on four MS&E courses consisting of

    • Three core courses, including one optimization course and one stochastics course

    • One 300-level elective MS&E course

  • Oral Qualification Examination (due end of the winter quarter of a student's second year): the student must complete a qualification exam consisting of an oral research presentation to three faculty members (including the PhD research advisor, at least one rotation advisor, and at least one MS&E faculty member). The presentation must include original research or promising directions toward original research. The evaluation will take 90 minutes, with 45 minutes of prepared material and 45 minutes of questions from faculty evaluators either during or after the presentation.

Students with a compelling reason may petition for extension on these requirements before the beginning of Spring Quarter in their second year.

Course List

Requirements:

  • At least one class from the optimization core

  • At least one class from the stochastics core

  • At least three core classes in total

  • At least six core or elective classes in total, with at least four listed or cross-listed in MS&E

Optimization Core (at least one of the following classes):

course

Linear Programming

course

Optimization

course

Optimization Algorithms

Stochastics Core (at least one of the following classes):

course

Stochastic Symptoms

course

Stochastic Calculus and Control

Additional Core Classes (one additional class from the optimization core, stochastics core, or the following list):

course

Discrete Mathematics

course

Approximation Algorithms

course

Stochastic Simulation

course

Stochastic Methods in Engineering

course

Topics in Causal Inference

course

Computational Social Choice

course

Financial Statistics

course

Mechanism and Market Design

Electives (at least three additional classes from the core above or the following lists):

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

  • Any MS&E 300-level non-seminar class; examples include:

course

Almost Linear Time Graph Algorithms

course

Optimal Transport in Operations Research, Statistics, and Economics

course

Advanced Topics in Game Theory with Engineering Applications

course

Law, Order, and Algorithms

course

Security and Risk in Computer Networks

course

Topics in Social Data

course

Queueing and Scheduling in Processing Networks

course

Network Structure and Epidemics

course

Reinforcement Learning: Frontiers

course

Market Design for Non-Profits

  • Any CS 300-level, EE 300-level, OIT 600-level, or STATS 300-level non-seminar course (at most two in total of those that are not cross-listed in MS&E); examples include:

course

Convex Optimization

course

Performance Engineering of Computer Systems and Networks

course

Theory of Probability I

Organizations, Strategy, Innovation, and Entrepreneurship

In their first two years in the Ph.D. program, all students are expected to work with faculty on research.  To ensure an early start, all students must work at least 25% of their time in their first year as a research assistant with a faculty member. Students on fellowships can earn course credit for the work. With approval from the students' adviser, one quarter of the requirement may be fulfilled by working as a Course Assistant (CA).

Ph.D. students in organizational behavior must take 3 courses in statistics and research methods, two of these courses must be statistics courses, and a minimum of 2 advanced-content courses chosen with input from their adviser.

Students are expected to complete a yearly plan, of no more than two typed pages in length, detailing the student's plans for the next year in terms of education (e.g., courses and seminars), research (e.g., RAships), and teaching (e.g., TAships).  This plan should be provided to the students' academic adviser for review no later than May 15 each calendar year.

Course List

Foundation in Organizational Behavior (five courses):

course

Seminar on Organizational Theory

Plus three of the following, which must include at least one 37x course and one 38x course:

course

Current Topics in Strategy, Innovation and Entrepreneurship

course

Innovation and Strategic Change

course

Entrepreneurship Doctoral Research Seminar

course

Strategy Doctoral Research Seminar

course

Groups and Teams

Statistics and Research Methods (examples; three courses required)

course

Introduction to Computational Social Science

course

Statistical Methods for Behavioral and Social Sciences

course

Sociological Methodology I: Introduction

course

Sociological Methodology II: Principles of Regression Analysis

course

Sociological Methodology III: Models for Discrete Outcomes

course

New Models and Methods in the Social Sciences

Policy and Strategy

The Policy and Strategy (P&S) Area addresses policy and strategy questions in a variety of organizational and societal settings. In order to approach interdisciplinary research questions in application domains as diverse as energy, environment, health, information technology, innovation, and government regulation, P&S faculty members rely on a broad range of analytical and empirical tools, such as decision analysis, optimization and operations research methods, formal economic modeling, econometrics, case studies, and simulation. After having been exposed to foundational knowledge of economics, strategy, and organizational theory, doctoral students in the P&S Area can select from a variety of courses to deepen their understanding of the specific application domains. The P&S Area's mission is to provide a first-class learning and research environment preparing doctoral students for careers at research universities, government institutions, and in the private sector.

The Policy and Strategy Qualification Procedure consists of two quarter-long research tutorials with two faculty members, and a presentation of the research with a minimum of three faculty members present.

Course List

Foundation in Policy and Strategy (three):

course

Economic Analysis

course

Strategy Doctoral Research Seminar

or course

Doctoral Research Seminar in Health Systems Modeling

or course

Doctoral Research Seminar in Energy-Environmental Systems Modeling and Analysis

Statistics and Research Methods (three):

course

Dynamic Systems

course

Introduction to Optimization

or course

Introduction to Optimization (Accelerated)

or course

Introduction to Optimization Theory

course

Mathematical Programming and Combinatorial Optimization

course

Stochastic Modeling

course

Simulation

course

Decision Analysis II: Professional Decision Analysis

course

Statistical Methods for Behavioral and Social Sciences

course

Sociological Methodology III: Models for Discrete Outcomes

The student must select a program of four or more electives including disciplinary depth courses that reflects his or her interests and this approved by the Policy and Strategy faculty. The following are a number of sample programs:

Sample Program: Modeling Emphasis

Research Methods

course

Dynamic Systems

course

Decision Analysis I: Foundations of Decision Analysis

course

Optimization

course

Stochastic Systems

Domain Depth

course

Health Policy Modeling

course

Analysis of Costs, Risks, and Benefits of Health Care

Two of the following:

course

Healthcare Operations Management

course

Healthcare Systems Design

course

Economics of Health and Medical Care

course

Advanced Decision Science Methods and Modeling in Health

Sample Program: Economics Emphasis

Research Methods

course

Contracts, Information, and Incentives

course

Game Theory and Economic Applications

Domain Depth

course

Industrial Organization 1

course

Matching and Market Design

Sample Program: Strategy Emphasis

Research Methods

course

Directed Reading and Research (Methods Apprenticeship)

course

Social Network Methods

Domain Depth

course

Innovation and Strategic Change

course

Strategy Doctoral Research Seminar

course

Economic Sociology

Sample Program: Risk Analysis Emphasis

Research Methods

course

Engineering Risk Analysis

course

Introduction to Stochastic Control with Applications

course

Decision Analysis I: Foundations of Decision Analysis

Influence Diagrams and Probabilistics Networks

Domain Depth

course

Project Course in Engineering Risk Analysis

course

Decision Analysis III: Frontiers of Decision Analysis