The Department of Statistics offers an exciting and
recently revamped Ph.D. program that involves students in cuttingedge
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 webbased 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 worldclass 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 datadriven
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 datacentric
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.
