From Molecules to Infrastructures: Transforming Data to Decisions using Geometry, Optimization, and Machine Learning

Abstract:  

We discuss how geometry, optimization, and machine learning are key technologies that are revolutionizing the way we think about data and the way we transform data into actionable models and decisions. Specifically, we explain how complex data (e.g., text, molecules, time series, images/video, supply chain flows) can be represented as geometrical objects and how this facilitates interpretation and extraction of useful information from data. We also discuss how extracted information can be mapped into decisions using optimization and machine learning models. We illustrate how to use these powerful math tools in innovative ways for analyzing complex datasets arising in molecular simulations, energy systems, and supply chains. Specifically, we show that these tools can help link the microstructure of materials to their rheological properties, can help analyze detect and trace environmental contaminants (such as PFAS), and can help optimize large-scale infrastructure networks.

Bio:

Victor M. Zavala is the Baldovin-DaPra Professor in the Department of Chemical and Biological Engineering at the University of Wisconsin-Madison and a senior computational mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory. He holds a B.Sc. degree from Universidad Iberoamericana and a Ph.D. degree from Carnegie Mellon University, both in chemical engineering. He is an associate editor for ACS-I&ECR and is on editorial board of the journals Mathematical Programming Computation and Computers & Chemical engineering. He is a recipient of NSF and DOE Early Career awards and of the Presidential Early Career Award for Scientists and Engineers (PECASE). His research interests include data science, control, and optimization and applications to chemical, energy, and environmental systems.