In the Department of Statistics graduate program, we aim to develop statisticians not only for academia, but also ones who will become leaders in endeavors such as medicine, law, finance, technology, government, and industry. Our graduate program is a stepping stone to a successful career in statistics. Our graduates have an outstanding placement record, having had their choice of careers in academia, banking and financial services, information technology, medical research, economic research and public policy. Several of our past PhD students have made their own marks in the academic world of statistics through development of fundamental statistical methodology.
The PhD Program
A unique aspect of our PhD program is our integrated and balanced training, covering research, teaching, and career development.
The department encourages research in both theoretical and applied statistics. Faculty members of the department lead the field in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics, and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education, and many others.
Two carefully designed special courses offered to PhD students form a unique feature of our program. Among these, Stat 303 equips students with the basic skills necessary to teach statistics, as well as to be better overall statistics communicators. Stat 366 equips them with skills necessary for research and scientific communication.
Our PhD students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every PhD candidate who passes the qualifying exam gives a 30-minute presentation each semester (in Stat 300), in which the faculty ask questions and make comments.
Please note: GSAS admitted the last cohort of AM students in statistics in the fall of 2017. Applications will no longer be accepted for the AM program in statistics.
The minimum mathematical preparation for admission to graduate study in statistics is linear algebra and advanced calculus. Ideally, each student’s preparation should include at least one term each of mathematical probability and mathematical statistics. Additional study in statistics and related mathematical areas, such as analysis and measure theory, is helpful. In the initial stages of graduate study, students should give high priority to acquiring the mathematical level required to satisfy their objectives.
Successful applicants demonstrate that they understand what the discipline of statistics entails, and show evidence of involvement in applications or a strong theoretical interest. They are able to articulate a strong motivation for studying statistics.
As statistics is so intimately connected with computation, computation is an important part of almost all courses and research projects in the department. Preferably, students should have programming experience relevant for statistical computation and simulation.