This Research Training Group (RTG) project supports creation of a dynamic, interactive, and vertically integrated community of students and researchers working together in computational and applied mathematics and statistics. The 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 data-driven approaches in which statistical inference and optimization play key roles. These developments are transforming research agendas throughout statistics and applied mathematics, with fundamental problems in analyzing data leading to new areas of mathematical and statistical research. A result is a growing need to train the next generation of statisticians and computational and applied mathematicians in new ways, to confront data-centric problems in the natural and social sciences.
The research and educational activities of the project lie at the interface of statistics, computation, and applied mathematics. The research includes investigations in chemistry and molecular dynamics, climate science, computational neuroscience, convex and nonlinear optimization, machine learning, and statistical genetics. The research team is made up of a diverse group of twelve faculty, including researchers at Toyota Technological Institute at Chicago and Argonne National Laboratory. The RTG is centered on vertically integrated research experiences for students, and includes innovations in both undergraduate and graduate education. These include the formation of working groups of students and postdocs to provide an interactive environment where students can actively explore innovations in computation, mathematics, and statistics in a broad range of disciplines. Post-docs will assume leadership roles in mentoring graduate students and advanced undergraduates. Participants in the RTG will receive an educational experience that provides them with strong preparation for positions in industry, government, and academics, with an ability to adopt approaches to problem solving that are drawn from across the computational, mathematical, and statistical sciences.
