Nick Sahinidis is Butler Family Chair and Professor of Industrial & Systems Engineering and Chemical & Biomolecular Engineering at the Georgia Institute of Technology. Dr. 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 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, and the Editor-in-Chief of Optimization and Engineering.
This talk presents recent theoretical, algorithmic, and methodological advances for black-box optimization problems for which optimization must be performed in the absence of an algebraic formulation, i.e., by utilizing only data originating from simulations or experiments. We investigate the relative merits of optimizing surrogate models based on generalized linear models and deep learning. Additionally, we present new optimization algorithms for direct data-driven optimization. Our approach combines model-based search with a dynamic domain partition strategy that guarantees convergence to a global optimum. Equipped with a clustering algorithm for balancing global and local search, the proposed approach outperforms existing derivative-free optimization algorithms on a large collection of problems.