Statistics - PhD
Division: Physical Sciences
Degree Type: PhD
The Department of Statistics offers an exciting and revamped PhD program that prepares students for cutting-edge interdisciplinary research in a wide variety of fields. The field of 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. In light of this, the Department of Statistics is currently undergoing a major expansion of approximately ten new faculty into fields of Computational and Applied Mathematics. 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 aims to prepare students to address these issues through rigorous training in theory, methodology, and applications of statistics, rigorous training in scientific computation, and research projects in core methodology of statistics and computation as well as in a wide variety of interdisciplinary fields.
During their first year, students are given a thorough grounding in material that forms the foundations of modern statistics and scientific computation, including data analysis, mathematical statistics, probability theory, applied probability and modeling, and computational methods. Throughout the entire program students attend a weekly consulting seminar where researchers from across the university come to get advice on modeling, statistical analysis, and computation. This seminar is often the source of interesting and ongoing research projects.
In the second year, students have a wide range of choices of topics they can pursue further, based on their interests, through advanced courses and reading courses with faculty. During the second year, students will typically identify their subfield of interest, take some advanced courses in the subject, and interact with the relevant faculty members. The Department maintains very strong connections to numerous other units on campus, either through joint appointments of the faculty or through ongoing collaborations. Students have easy access to faculty in other departments, which allows them to expand their interactions and develop new interdisciplinary research projects. Examples include joint projects with Human Genetics, Ecology and Evolution, Neurobiology, Chemistry, Economics, Health Studies, and Astronomy.
The research domains of current faculty are:
Probability: Jian Ding, Steve Lalley, Greg Lawler
Mathematical Statistics: Peter McCullagh, Michael Stein, Stephen Stigler, Wei-Biao Wu
Statistical Methodology: Peter McCullagh, Mary Sara McPeek, Debashis Mondal, Per Mykland, Dan Nicolae, Michael Stein, Stephen Stigler, Wei-Biao Wu
Scientific Computation: Mihai Anitescu, Lek Heng Lim, Ronald Thisted, Jonathan Weare (Math Department)
Statistics and Genetics: Mary Sara McPeek, Dan Nicolae, John Reinitz, Matthew Stephens
Computational Neuroscience: Yali Amit, Nicolas Brunel
Biostatistics: Ronald Thisted
Machine Learning and Pattern Recognition: Yali Amit, Risi Kondor, John Lafferty, Lek-Heng Lim
Environmental and Spatial Statistics: Mihai Anitescu, Michael Stein
Computational Chemistry: Jonathan Weare
Mathematical Finance and Econometrics: Lars Hansen, Per Mykland, Wei-Biao Wu
Applications should be initiated through the online application.
Deadline for admissions and financial aid: December 31
The Department does not interview applicants, but you are welcome to visit campus and sit in on classes.
The Department offers both an MS and a PhD.
- Online Application
- Transcript: An official copy of your transcript from each undergraduate or graduate institution you have attended is required. If it is unclear from your transcript whether you have met the prerequisites for our program, we recommend that you include a list of the topics covered in each course that would be relevant to our program. If the original language of your transcripts is not English, you must obtain an official translation and submit official transcripts both in the original language and in English.
- Letters of Recommendation: A minimum of three letters of recommendation are required. Two additional letters may be included if you think the circumstances warrant it.
- GRE scores: GRE scores are required for all applicants. Applicants to the doctoral program are strongly encouraged to submit GRE Mathematics Subject Test scores as well. The University’s institution code is 1832; our department code is 0705.
- TOEFL scores: TOEFL scores are required for all applicants except those who grew up in the United Kingdom, Canada, Ireland, Australia, New Zealand, South Africa, or the United States;and applicants who, in the last five years, completed one academic year of full-time study at an English-language institution in one of those countries. The University’s institution code is 1832; our department code is 59.
- Candidate Statement: Your statement should explain what interests you about statistics, what your goals are, and what you hope to accomplish in your studies here.
- Application Fee: A waiver can be considered for domestic applicants only. Students applying to both the MS and PhD programs must pay two fees.
Admissions decisions are emailed in March.
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.
Given the diverse backgrounds of the students, the program is flexible in the timing and content of coursework and research. The following describes a typical path for a student with a solid background in mathematics and some familiarity with statistics. During the first year, the student takes three of the following sequences: probability (STAT 30400, 38100, 38300), mathematical statistics (STAT 30400, 30100, 30200), applied statistics (STAT 34300, 34500, 34700), and computational mathematics and machine learning (STAT 30900, 31000, 37700). At the start of the 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). The choice of sequences and prelims will be done in coordination with the Director of Graduate Studies. During the second year, students take more advanced and specialized courses, depending on their interests. The selection of courses offered varies from year to year, but there is always a variety of courses in probability, in theoretical and applied statistics, in machine learning and in computational mathematics. By the end of the second year, most students should have begun to work with a thesis advisor, usually after taking a reading course with one—or more—prospective advisors. After making substantial research progress, and no later than the end of autumn quarter of the fourth year, the student will identify a thesis committee consisting of an advisor and two other faculty members and will prepare a thesis proposal that is presented to the committee and must be approved. A completed dissertation is presented in a formal departmental seminar, and then a final oral examination completes the program for the Ph.D. 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.
Costs & Financial Aid
In recent years, our department has been able to provide full support (tuition plus a stipend) for most of its PhD students and we expect this to continue for the foreseeable future. Typically students are supported for at least four years. Support is not tied to work with a particular faculty member. At present, most fifth-year students receive full support and most PhD students receive summer support.