Hao-Jun Michael Shi is a Research Scientist at FAIR, Meta Superintelligence Labs. He obtained his B.S. degree in Applied Mathematics from the University of California, Los Angeles, and his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University, where he was advised by Jorge Nocedal. He works at the intersection of optimization and artificial intelligence, bridging theory and practice. Most recently, he has focused on scalable and distributed training algorithms for deep learning. He was one of the lead developers, with Tsung-Hsien Lee, of the open-source PyTorch implementation of the Distributed Shampoo optimizer, which won first place in the external tuning ruleset of the MLCommons AlgoPerf benchmark. He has previously contributed to stochastic, noisy, and derivative-free optimization, as well as recommender systems and embedding compression. He received the Walter P. Murphy Fellowship and the NSF Graduate Research Fellowship Honorable Mention in 2016 and 2017, and was named a top ICML reviewer in 2019.