Andrew White, a leader in the adaption of deep learning to chemistry and materials, will discuss "Deep learning for materials design with a few data points," in a guest lecture presented by the Knight Campus Department of Bioengineering. The talk is scheduled for 11 a.m. on Monday, Nov. 7 in the Beetham Family Seminar Room.
An associate professor of chemical engineering at the University of Rochester, White studies the self-assembly and structure of biomacromolecules with coarse-grained simulation and deep learning.
Deep learning, White says, has begun a renaissance in chemistry and biology. We can devise and fit models to predict molecular properties in a few hours and deploy them in a web browser. We can create novel generative models that were previously PhD theses in an afternoon. We’re exploring deep learning in soft materials and proteins. We are focused on two major problems: interpretability and data scarcity. Now that we can make deep learning models to predict molecular properties ad nauseum, what can we learn?
White will discuss his team's recent efforts on interpreting deep learning models through counterfactuals. Data scarcity is another common problem as problems become closer to translation medicine: how can we learn new properties without significant expense of experiments? One method is the judicious choice of experiments, which can be done with active learning. Another approach is self-supervised learning and constraining symmetries, which both try to exploit structure in data. White will cover recent progress in these areas. Finally, one consequence of the state of deep learning is that you can just make cool things. White will review a few fun projects, including making molecules by banging on the keyboard, doing molecular dynamics constrained on space groups, and doing math with emojis.
Andrew White graduated from Rose-Hulman Institute of Technology in 2008 with a BS in chemical engineering. While at Rose, he spent a year studying at the Otto-von Guericke Universität and the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Dr. White completed a PhD in chemical engineering at the University of Washington in 2013. The thesis topic was the creation of non-fouling biomimetic surfaces with computational modeling. Dr. White worked with Professor Greg Voth at University of Chicago as a Post-doctoral fellow in the Institute for Biophysical Dynamics from 2013-2014. In Chicago, he developed new methods for combining simulations and experiments. Dr. White joined the University of Rochester in Chemical Engineering in 2015 and is currently an associate professor. He has joint appointments in the Chemistry Department, Biophysics, Materials Science, and Data Science programs. Dr. White received a National Science Foundation CAREER award in 2018 and an Outstanding Young Investigator Award from the National Institutes of Health in 2020. Dr. White has authored a textbook on deep learning for molecules and materials, which is freely available at https://dmol.pub