The session invites papers in the general area of data-driven and surrogate-based optimization. Topics on theory, algorithms and software for derivative-free optimization, surrogate modeling and optimization, black-box and grey-box optimization, machine learning/AI-embedded optimization, etc., are of interest. Application papers offering insights into the interplay between data-driven optimization theory and practice are also encouraged.
Co-Chairs
Zhe Wu, University of California Los Angeles
03:30 PM
Wei-Ting Tang, Joel Paulson
03:51 PM
Joshua Hang Sai Ip, Georgios Makrygiorgos, Joel Paulson, Ali Mesbah
04:12 PM
Hao Chen, Gonzalo Constante-Flores, Can Li
04:33 PM
Mujin Cheon, Calvin Tsay, Dong-Yeun Koh, Jay Hyung Lee
04:54 PM
Yen-An Lu, Wei-Shou Hu, Joel Paulson, Qi Zhang
05:15 PM
Lifeng Zhang, Tanuj Karia, Gustavo Chaparro, Benoit Chachuat, Claire S. Adjiman
05:36 PM
Tom Savage, Ehecatl Antonio del Rio Chanona