About

Overview

The big-data revolution has transformed many areas of engineering, industry, mathematics, and science. This NSF funded Research Training Grant focuses on

• conducting research on the mathematical foundations and applications of data science,

• broadening and enhancing the scope and quality of the educational and research training provided to graduate students and postdoctoral fellows, and

• providing more opportunities for undergraduate students, particularly students from historically underrepresented groups, to participate in courses and research experiences in applied mathematics.

Research

Our research projects have a strong interdisciplinary flavor, combining fundamental stochastic, statistical, combinatorial, dynamical, and computational aspects with concrete applications. Projects will involve collaborations with domain scientists from other disciplines, including astrophysics, biology, engineering, and neuroscience. Sample research topics include applying machine learning and Bayesian statistics tools to deriving, analyzing, and simulating partial differential equations; designing optimal closed-loop experiments using statistical inference; advancing techniques in discrete optimization; developing combinatorial models in neuroscience; understanding random projections of high-dimensional measures; and constructing dimension reduction techniques that preserve relevant structure of large data sets.

Training

The educational activities focus on vertically integrated training of undergraduates, graduate students, and postdoctoral fellows, including

• first-year seminars focused on data science and social justice,

• enhanced undergraduate and graduate curricula,

• summer research experiences for undergraduate students, and

• working groups for advanced graduate students and postdoctoral fellows.

Outcomes

We will post sample course materials and descriptions of undergraduate research on this website. Publications that resulted from this grant can be found at the NSF Award website.