Optimization and Machine Learning Nexus: Model-Based and Data-Driven Approaches
The Texas A&M Energy Institute’s Distinguished Lecture Series in Energy will feature Dr. Nick Sahinidis, a Professor of Industrial & Systems Engineering and Chemical & Biomolecular Engineering at the Georgia Institute of Technology, on Wednesday, March 5, 2025, from 11:00 a.m. – 12:00 p.m. CST (UTC -6:00). The topic will be “Optimization and Machine Learning Nexus: Model-Based and Data-Driven Approaches.”
Biography
Dr. Nick Sahinidis is the Butler Family Chair and a Professor of Industrial & Systems Engineering and Chemical & Biomolecular Engineering at the Georgia Institute of Technology. He previously taught at the University of Illinois at Urbana-Champaign from 1991 to 2007 and Carnegie Mellon University from 2007 to 2020. He has pioneered algorithms and developed widely used software for optimization and machine learning. He received the INFORMS Computing Society Prize in 2004, the Beale-Orchard-Hays Prize from the Mathematical Programming Society in 2006, the Computing in Chemical Engineering Award in 2010, the Constantin Carathéodory Prize in 2015, and the National Award and Gold Medal from the Hellenic Operational Research Society in 2016. He is a member of the US National Academy of Engineering, a fellow of INFORMS, a fellow of AIChE, a fellow of the Asia-Pacific Artificial Intelligence Association, and past Editor-in-Chief of Optimization and Engineering.
Abstract
We present recent methodological developments for three closely related optimization and machine learning problems:
- Global optimization of algebraic models.
- Model building from data while enforcing shape constraints.
- Optimization with data originating from simulations or experiments.
We explore the connections between these three problems and discuss their applications in various fields of science and engineering.