Speaker
Thiago Serra Azevedo Silva, Tippie College of Business, University of Iowa

Abstract
Constraint learning is a new paradigm of modeling optimization problems with the help of machine learning. For example, we can learn constraints empirically from available data, or we can avoid writing a nonlinear objective function by learning a simpler approximation for it. We usually achieve that by using a neural network as part of the optimization model. As a consequence, we need to design optimization algorithms that take into account the structure of neural networks. We show how polyhedral theory and network pruning can help us solve those problems more efficiently, but that's just the beginning!

Bio
Thiago Serra is an assistant professor of business analytics at the University of Iowa. His scholarship is focused on the theory, practice, and integration of machine learning and mathematical optimization. Previously, he was an assistant professor at Bucknell University, a visiting research scientist at Mitsubishi Electric Research Labs, and an operations research analyst at Petrobras. He has a Ph.D. in operations research from Carnegie Mellon University, from which he received the Gerald L. Thompson Doctoral Dissertation Award in Management Science. He also has a master's degree in computer science from the University of Sao Paulo (USP) and a computer engineering degree from the University of Campinas (Unicamp). He is the chair of the INFORMS Computing Society and serves as an associate editor for the journals International Transactions in Operational Research and INFORMS Journal on Data Science.