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. In the second part of this talk, I will describe our use of deep learning to estimate slow collective variables from molecular simulation trajectories and the use of these coordinates to train highly efficient latent space molecular simulators. By combining these two ideas it is possible to extract low-dimensional projections of experimental data onto a low-dimensional latent manifold and interpretably reconstruct all-atom molecular configurations from these projections.
Andrew Ferguson is an Associate Professor at the Pritzker School of Molecular Engineering at the University of Chicago. He received an M.Eng. in Chemical Engineering from Imperial College London in 2005, and a Ph.D. in Chemical and Biological Engineering from Princeton University in 2010. From 2010 to 2012 he was a Postdoctoral Fellow of the Ragon Institute of MGH, MIT, and Harvard in the Department of Chemical Engineering at MIT. He commenced his independent career in the department of Materials Science and Engineering at the University of Illinois at Urbana-Champaign in August 2012, and was promoted to Associate Professor of Materials Science and Engineering and Chemical and Biomolecular Engineering in January 2018. He joined the Institute for Molecular Engineering in July 2018. His research uses theory, simulation, and machine learning to understand and design self-assembling materials, macromolecular folding, and antiviral therapies. He is the recipient of the Institution of Chemical Engineers 2018/19 Junior Moulton Medal, a 2017 UIUC College of Engineering Dean's Award for Excellence in Research, 2016 AIChE CoMSEF Young Investigator Award for Modeling & Simulation, 2015 ACS OpenEye Outstanding Junior Faculty Award, 2014 NSF CAREER Award, 2014 ACS PRF Doctoral New Investigator, and was named the Institution of Chemical Engineers North America 2013 Young Chemical Engineer of the Year.
Back to Workshop II: Interpretable Learning in Physical Sciences