Ph.D. Program

The Ph.D. is formulated to prepare students for research and teaching. The aim of the program is to provide a strong disciplinary background in one of the core areas of research in the department. The emphasis is on the theoretical foundations, mathematical models, and computational issues in practical problem solving. Current teaching and research activities include probability and stochastic processes, stochastic analysis, mathematical statistics, linear and nonlinear optimization, stochastic optimization, convex analysis, stochastic networks and queueing theory, mathematical and computational finance, and financial econometrics. Application areas of current interest to faculty include finance, energy, health, bioinformatics, and engineering problems. To learn more about the research interests of individual faculty members, please visit the Research section.

The departmental faculty are affiliated with a number of interdisciplinary programs and centers, including the Program in Applied and Computational Mathematics, the Bendheim Center for Finance, the Andlinger Center for Energy and the Environment, the Princeton Environmental Institute, and the Center for Statistics and Machine Learning. Students may combine their departmental work with courses and research opportunities offered by such programs and centers and also by other departments including Computer Science, Economics, and Mathematics.

About half of PhD recipients in Operations Research & Financial Engineering accept positions in academia. See this list of first positions of our graduating PhD students.


  • Multiple students eating lunch in the Statistics Laboratory
    Statistics Laboratory Research Group
  • Oxford-Princeton Workshop
  • Students gathered with Prof. Massey at a meeting of the Wesley L. Harris Scientific Society.
    Wesley L. Harris Scientific Society

Curriculum

In the first year of the Ph.D. program, students enroll in the 6 departmental core courses in probability, statistics, and optimization in consultation with the Department's Director of Graduate Studies, to be followed by a qualifying exam at the end of the summer. In addition, at least two advanced courses and two semesters of directed research are completed under the direction of a faculty advisor in the student's area of interest by the end of the second year in preparation for the general examination. The general examination is normally taken in the Spring of the second year of study. Usually, beyond the general examination, two to three years are needed for the completion of a suitable dissertation. Upon completion of theses studies and acceptance of the dissertation by the department, the candidate is admitted to the final public oral examination.

A comprehensive guide to the ORFE Ph.D. program can be found in the Graduate Student Handbook and in the Graduate School Catalog.

Research Seminars

The weekly departmental colloquium series brings distinguished researchers to the department to present their latest work. In addition, informal research seminars are organized in order to exchange information and to discuss ideas arising from the research work in progress. Students, research staff, visiting scholars, and faculty members participate in these seminars.

Application

A bachelor's degree in engineering, sciences, or mathematics is normally required for admission to the graduate program. A strong mathematical background is expected for admission. The Graduate Record Examination (GRE) results will be optional (not required) for Fall 2021. Applicants whose native language is not English or who have not received their undergraduate education in a U.S. college or university should also submit the results of the Test of English as a Foreign Language (TOEFL) or IELTS.

A limited number of Princeton application fee waivers may be available. Please contact Associate Dean Julie Yun at julieyun@princeton.edu if you are interested.

For further details on applying to the graduate program, please visit the Graduate School website.

Financial Support

  • Professor Ahmadi and Graduate Students in Bordeaux
  • Students at dinner together.
    1st Year Graduate Students Enjoying Dinner Together
  • Professor Shkolnikov at Oxford-Princeton Conference with Colleague
    Professor Shkolnikov at Oxford-Princeton Conference with Colleague

The department aims to support all doctoral students requesting aid through a combination of fellowships and assistantships. All first-year Ph.D. candidates are supported by full-time fellowships, allowing students to focus on courses and providing flexibility in the choice of a research advisor. From the second year onward, students are supported by a combination of teaching assistantships, research assistantships, and fellowships. Continuation of support is recommended on the basis of satisfactory progress.

Further details on financial support can be found on the Graduate School website.

Research Labs

The Department features several research groups that facilitate collaborations between students and faculty and provide a variety of resources for research and teaching. These include the following:

Financial Engineering Laboratory. This laboratory holds the computers, software, and financial data feeds needed for teaching and research in financial engineering. It is a focal point for graduate students in the Ph.D. program in financial engineering and M.Fin. It also serves as a gateway to collaborative research projects with financial institutions.

Statistics Laboratory and Financial Econometrics Laboratory. Research emphasizes statistical methods in financial econometrics and risk management, computational biology, biostatistics, high-dimensional statistical learning, data-analytic modeling, longitudinal and functional data analysis, nonlinear time series, wavelets and their applications, among others. Our primary research focuses on developing and justifying statistical methods that are used to solve problems from the frontiers of scientific research.

Transportation Center. The center conducts research on information and decision engineering technologies and how these technologies can be used to improve transportation-related decision making.