Events

Andrew Ferguson, University of Chicago

Reconstructing All-Atom Protein Folding from Low-Dimensional Experimental Time Series

Data-driven modeling and machine learning present powerful tools that are opening up new paradigms and opportunities in the understanding, discovery, and design of soft and biological materials. In the first part of this talk, I will describe an approach integrating ideas from dynamical systems theory and nonlinear manifold learning to reconstruct multidimensional protein folding funnels from the time evolution of single experimentally measurable observables.

Dr. Tomáš Bzdušek, Paul Scherrer Institute and University of Zurich

Mathematics of Topological Insulators and Semimetals

Many properties of crystalline materials, such as conductivity or the tendency to become magnetically ordered at low temperatures, derive from their so-called “electronic band structure.” Although this is an established notion in solid state physics, dating back to the early days of quantum mechanics, our understanding of electronic band structure has been greatly challenged and revolutionized over the past 15 years by the discovery of so-called topological materials.

Gregory Rudnick, University of Kansas

The Life Cycle of Galaxies in Clusters Over 10 Billion Years

Galaxies live in a range of environments, characterized by their volume density. For example, the densest regions of the Universe are in galaxy clusters, which contain hundreds to thousands of galaxies all in pseudo gravitational virial equilibrium. These dense environments can in turn alter the properties of the galaxies themselves in striking ways via a variety of gravitational and hydrodynamic processes. The result of these processes alter galaxy shapes, their internal dynamics, and shuts off the formation of new stars.