Statistics and Applied Probability

The Department of Statistics and Applied Probability offers a Ph.D. program in which students develop a broad understanding of the theory and practice of probability and statistics. (Information about the MA program and BS/MS program is provided on other pages.)

The Ph.D. program prepares students to conduct innovative research, beginning with a solid foundation in course work and leading towards original dissertation research. Research topics lie broadly in stochastic modelling, data science and machine learning, with diverse areas, such as financial mathematics, big data analytics, computational methods, artificial intelligence, biostatistics and probabilistic theory underpinning algorithms and applications. Interdisciplinary collaboration areas include computer science, environmental science, physics, mathematics, biological and biomedical sciences, engineering, and finance. A brief list of the faculty's research areas may be found at https://www.pstat.ucsb.edu/about/research.

Our students have access to a variety of outstanding resources. For students interested in applying probabilistic methods to the area of finance, for instance, the department hosts the Center for Financial Mathematics and Actuarial Research, which provides national and international leadership in research into quantitative finance from many different perspectives by bringing together faculty, students, and visitors from other departments, universities, and companies for distinguished lectures, seminar series, and conferences. The department has excellent computing facilities, as well as access to UCSB Center for Scientific Computing at California NanoSystems Institute, and the Data Lab for statistical consulting. We offer advanced graduate courses in data science and machine learning, including PSTAT 231 (Introduction to Statistical Machine Learning), PSTAT 232 (Computational Techniques in Statistics), PSTAT 234 (Statistical Data Science) and PSTAT 235 (Big Data Analytics).

A key component of our program is cutting-edge research opportunities under close supervision of a faculty advisor. The department provides methodological and interdisciplinary research opportunities through collaboration with various centers at UCSB, including the Center for Financial Mathematics and Actuarial Research, Data Science Initiative, Center for Responsible Machine Learning, Neuroscience Research Institute, Center for Control, Dynamic Systems and Computation, and Center for BioPolymers, Automated Cellular Infrastructure, Flow and Integrated Chemistry Materials Innovation Platform. The department provides full funding support to admitted students, through teaching assistantship, research grant, and scholarship from academia and industry.

Admission to the Ph.D. program is to the core Ph.D. program, giving students flexibility later to petition for admission to one of three interdisciplinary Ph.D. emphases: Financial Mathematics and Statistics (FMS), Quantitative Methods in the Social Sciences (QMSS), and Bioengineering. These optional emphases are not required for the core Ph.D. degree program, but the emphases may be extremely valuable for students whose research focuses on one of these areas.