The application deadline for our doctoral program is 11:59 p.m. PST on December 5, 2017.
Applicants may view their application status and decision on the application activity page. Log back into your online application, and click on "Review Your Activity". We notify applicants of decisions in February.
Current Stanford graduate students should apply online using the Instructions for New Students, except that it is unnecessary to resend hard copies of transcripts or resend official test scores, as we have those from the first application. Please add any current Stanford degree to the application, and upload a current Stanford transcript (even if no grades are reported).
The Doctoral Program in Management Science and Engineering is a unique academic program that combines rigorous course work with deep immersion in cutting-edge research. The goals are to create important knowledge while addressing significant practical problems in a variety of domains. The department is broadly interdisciplinary, and combines engineering, mathematical modeling and algorithmic approaches, social sciences, and statistical and data analysis.
Please rank order 1-3 research areas in your application. Since the faculty begins review of the applications based on this information, please consult the information on these areas below to help you in your choice(s).
Optimization and Stochastics:
The Optimization and Stochastics area focuses on developing models and algorithms to gain new insights and make better decisions. The area is characterized by its mathematical depth, broad applicability, and interdisciplinary nature. It intersects with the design, analysis and application of algorithms, graph theory and combinatorics, computational probability, simulation, and queuing theory. PhD students typically take courses in optimization, stochastic processes, statistics, and computer science. The area has a close affiliation with ICME.
Mixed Methods in Social Systems
The Organizations area focuses on the study of work, mainly in technical settings and considers the organizational issues implicated at the intersection of work and technology. Students in the area take courses in sociology, social psychology, organizational theory, and organizational behavior as well as field research methods courses including ethnography, statistics, and social network analysis. Recent PhD dissertations include studies of global collaboration, flash teams, social movements, occupational identities, and collective innovation. PhD students are typically affiliated with the Center for Work, Technology, and Organizations (WTO).
Strategy, Innovation, and Entrepreneurship:
The Strategy, Innovation, and Entrepreneurship area focuses on innovation, competition, and collaboration by established firms, and the formation and growth of ventures. The area is characterized by its social science depth, state-of-the-art methods, and field-based understanding of technology firms and markets. PhD students take courses in organization theory, economics, sociology, entrepreneurship, and strategy as well as methods courses in statistics, experimental methods, inductive case studies, and computational tools. Recent PhD dissertations include studies of ecosystem strategy in the solar industry, platform competition, competitive interaction in the software industry, and CEO succession in family businesses. Students are typically affiliated with the Stanford Technology Ventures Program (STVP).
Computational Social Science:
Computational Social Science focuses on problems at the interface of social and computational science, especially in large-scale and data rich contexts. PhD students take diverse courses in statistics, computer science, optimization, economics, sociology, and possibly other social sciences. Areas of focus include analysis and design of public policy, crowdsourcing using complex work structures, coordination in online labor markets, and causal inference and experimentation. Recent PhD dissertations include design and analysis of a peer-to-peer credit network and reputation system, analysis of dynamic online markets, design and analysis of flash teams, and fast algorithms for large scale personalized recommendations. CSS students are typically affiliated with the Social Algorithms Lab (SOAL), Center for Work, Technology, and Organizations (WTO) and/or Stanford Technology Ventures Program (STVP).
The Operations Management area focuses on applying analytical, computational, and economic tools to address a wide variety of problems in business, government, and society. Areas of interest include design of market mechanisms such as organ allocation, auctions and platforms for marketplaces, design of complex networks, capacity planning, and inventory control. PhD students take advanced courses in optimization, microeconomics, game theory, stochastics, and topics in operations management as well other areas tailored to the interests of the student such as in Health Policy, Strategy, and Energy and Environmental Policy. Recent PhD dissertations include online auction pricing, designing school choice programs, designing kidney exchange clearinghouses, mechanism design, and sustainability in global supply chains. PhD students are often affiliated with one or more of the MS&E centers.
Decision and Risk Analysis:
The Decision and Risk Analysis area focuses on applying engineering systems analysis and probability to complex economic and technical design or management problems, in the private and public sectors. In the Decision Analysis group, recent dissertations include experiment sample sizes for influence diagrams, Markov process regression, and quantile function methods for decision analysis. The Engineering Risk Research Group (ERRG) focuses on complex engineered systems (e.g., optimal architecture of satellites and deflection of asteroids’ trajectories), cyber security, and risks in games against adversaries (e.g., counter-terrorism, counter-insurgency, and staying ahead of narco-traffickers). Courses include the mathematical foundations of modeling dynamic environments, value and management of uncertain opportunities and risks, and public policy and strategy applications. Risk analysis requires optimization, stochastic processes, economics and game theory courses.
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, computational mathematics, and computer science 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. PhD students in the area typically are affiliated with the Center for Financial and Risk Analytics (CFRA).
Energy and Environmental Policy:
The Energy and Environmental Policy area focuses on energy and environmental modeling such as integrated assessment of global climate change, mitigation/impacts/adaption, energy demand analysis including economic and/or behavioral factors associated with energy efficiency, market structure for electricity systems and environmental mitigation, behavioral economics related to energy, and comparative use of energy models through the Energy Modeling Forum. PhD students take courses in economic analysis, mathematical modeling, optimization, decision analysis, policy analysis, econometrics, and a variety of other courses, depending on the student's particular background and interests. Students typically become affiliated with the Energy Modeling Forum and/or the Precourt Energy Efficiency Center.
The Health Policy area focuses on the application of analytical, computational, and economic tools to inform decision making in health. Areas of interest include clinical decision making, public health decision making, and healthcare operations management. Students take courses in optimization, probability and statistics, economics, operations management, cost-effectiveness analysis, and health policy modeling, as well as a variety of other courses. Recent PhD dissertations include studies of managing uncertainty in medical decision making; resource allocation for infectious disease control; optimizing patient treatment decisions in the presence of rapid technological advances; and economic analysis of HIV prevention and treatment portfolios. PhD students in the Health Policy area are often affiliated with the Center for Health Policy /Program on Clinical Outcomes Research (CHP/PCOR) in the Medical School.
The Entrepreneurial Policy area focuses on the role of public policy in shaping the rate, nature and success of entrepreneurial activities and the formation of new firms. PhD students take courses in organization theory, economics, sociology, entrepreneurship and strategy as well as state-of-the art econometrics. Recent dissertations include studies of how institutional reforms such as changing bankruptcy laws influence the formation and growth of new firms in Japan, the effectiveness of accelerator policies such as Start-Up Chile, and the implications of educational and other reforms on entrepreneurial success in China. PhD students in the area typically are affiliated with the Stanford Technology Ventures Program (STVP).
National Security Policy:
See the Engineering Risk Research Group under Decision and Risk Analysis.