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Brian Bullins is an assistant professor in the Department of Computer Science at Purdue University, where he joined in Fall 2022. He previously served as a research assistant professor at the Toyota Technological Institute at Chicago. He obtained his Ph.D. in Computer Science from Princeton University in 2019, where he was advised by Elad Hazan and was supported by the Siebel Scholarship. He also received a B.S. degree in Computer Science from Duke University, earning recognition as a Benjamin N. Duke Scholar. His research interests broadly lie in the theory and practice of optimization and machine learning. He has focused on improving matrix estimation techniques and has developed novel higher-order methods for convex and non-convex optimization with provable guarantees. His ongoing work involves leveraging approaches for faster methods in highly parallel settings for distributed optimization, where his contributions were recognized with a best paper award at COLT 2021.
GRE is not required.