
In this lecture, 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 also explore the connections between these three problems and discuss their applications in various fields of science and engineering.
Nick Sahinidis is the Butler Family Chair and Professor of Industrial & Systems Engineering and Chemical & Biomolecular Engineering at the Georgia Institute of Technology. Sahinidis previously taught at the University of Illinois at Urbana-Champaign (1991–2007) and Carnegie Mellon University (2007–2020). He has pioneered algorithms and developed widely used software for optimization and machine learning. He has received several awards for his research including an NSF CAREER Award, 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 National Academy of Engineering (NAE), a fellow of INFORMS and AIChE. Sahinidis has served on the editorial boards of many leading journals and in various positions within AIChE (American Institute of Chemical Engineers). He has also held several positions within INFORMS (Institute for Operations Research and the Management Sciences), including Chair of the INFORMS Optimization Society.