Jiayi Guo is an assistant professor at Shanghai University of Finance and Economics. Dr. Guo received his Ph.D. degree from the School of Operations Research and Information Engineering at Cornell University (2018). Before coming to Cornell, he received his B.S. of Mathematics and B.S. of Computer Science dual degree (2012) at University of Illinois Urbana-Champaign.
Broadly conceived, Dr. Guo’s research area is optimization. Currently, his work explores different variations of iterative methods to solve continuous optimization problems on non-smooth and non-convex functions, and their application in machine learning. In general, Jiayi is interested in the interplay between optimization, simulation, and numerical solver development.
Lastly, Dr. Guo also had working experiences at eBay and Argonne National Laboratory, and consulting experiences with several leading enterprises.
Rescaling nonsmooth optimization using BFGS and Shor updates.
Guo, J., & Lewis, A. S. (2018).
Nonsmooth Variants of Powell’s BFGS Convergence Theorem.
Guo, J.,& Lewis, A. S. (2018).
SIAM Journal on Optimization, 28(2), 1301-1311.
Complexity results for the basic residency scheduling problem.
Guo, J.,Morrison, D. R., Jacobson, S. H., & Jokela, J. A. (2014).
Journal of Scheduling, 17(3), 211-223.