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Ph.D. Degree in Statistics

The Department of Statistics offers an exciting and recently revamped Ph.D. program that involves students in cutting-edge interdisciplinary research in a wide variety of fields. Statistics has become a core component of research in the biological, physical, and social sciences, as well as in traditional computer science domains such as artificial intelligence and machine learning. The massive increase in the data acquired, through scientific measurement on one hand and through web-based collection on the other, makes the development of statistical analysis and prediction methodologies more relevant than ever.

Our graduate program prepares students to address these issues through rigorous training in scientific computation, and in the theory, methodology, and applications of statistics. The course work includes four core sequences (of which students are required to take three, usually during their first year):

  • Probability (STAT 30400, 38100, 38300)
  • Mathematical statistics (STAT 30400, 30100, 30210)
  • Applied statistics (STAT 34300, 34500, 34700)
  • Computational mathematics and machine learning (STAT 30900, 31015/31020, 37710).

At the start of their second year, the student takes preliminary examinations covering two of these areas, one theoretical (probability or mathematical statistics) and one applied (applied statistics or computational mathematics). During the second and subsequent years, students can take more advanced courses, and perform research, with world-class faculty in a wide variety of research areas.

In recent years, a large majority of our students complete the Ph.D. within four or five years of entering the program. Students who have significant graduate training before entering the program can (and do) obtain their doctor's degree in three years.

Most students receiving a doctorate proceed to faculty or postdoctoral appointments in research universities. A substantial number take positions in government or industry, such as in research groups in the government labs, in communications, in commercial pharmaceutical companies, and in banking/financial institutions. The department has an excellent track record in placing new Ph.D.s.

Ph.D. Track in Computational and Applied Mathematics

The University of Chicago is building a community of researchers working in computational and applied mathematics (CAM) and statistics. The Department of Statistics recently hired several new faculty under the Computational and Applied Mathematics Initiative. This activity recognizes the ways in which applied mathematics and statistics are becoming increasingly integrated. For example, mechanistic models for physical problems that reflect underlying physical laws are being combined with data-driven approaches in which statistical inference and optimization play key roles. These developments are transforming research agendas throughout statistics and applied mathematics, and are impacting disciplines throughout the natural and social sciences.

A critical need now exists to train the next generation of computational and applied mathematicians to confront data-centric problems in the natural and social sciences. In response to these developments, the Department of Statistics is forming a new Computational and Applied Mathematics track within the Statistics Ph.D. program. The Department will offer a small number of exceptionally qualified students the choice to participate in this program.

The requirements of the Ph.D. Track in Computational and Applied Mathematics parallel those of the Ph.D. in Statistics. Together with an assigned advisor, students will select courses from core sequences and a diverse set of possible electives. Example topics include traditional areas such as partial differential equations, numerical analysis, and dynamical systems, as well as modern signal processing, machine learning, data collection (in particular imaging) and processing, optimization, stochastic modeling and analysis, and the statistical analysis of high dimensional data. Students will complete preliminary examinations in two chosen areas, typically in their second year of study. The track will be highly interdisciplinary, with many students interacting with at least one scientific domain.

Prerequisites for the Program

A student applying to the Ph.D. program normally should have taken courses in advanced calculus, linear algebra, probability, and statistics. Additional courses in mathematics, especially a course in real analysis, will be helpful. Some facility with computer programming is expected. Students without background in all of these areas, however, should not be discouraged from applying, especially if they have a substantial background, through study or experience, in some area of science or other discipline involving quantitative reasoning and empirical investigation. Statistics is an empirical and interdisciplinary field, and a strong background in some area of potential application of statistics is a considerable asset. Indeed, a student's background in mathematics and in science or another quantitative discipline is more important than his or her background in statistics.

To obtain more information about applying, see the Guide For Applicants.