Presentation Abstract
We discuss how advances in machine learning enable the development of technologies that can help foster a more sustainable and healthy world. Specifically, we discuss how supervised learning techniques can be used to design sensors capable of detecting dangerous environmental contaminants (e.g., ozone and nerve agents) at extremely low concentrations. We also discuss how unsupervised learning techniques can be used to quickly navigate vast databases of plastic waste and PFAS and how this can help identify technologies for their capture and destruction. In addition, we discuss how active learning techniques can be combined with high-throughput experimental platforms to design new electrochemical technologies for energy storage and chemical manufacturing. Our discussion will aim to highlight key mathematical concepts and computational tools that enable scalable solutions.
Speaker 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.
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