The Art of Designing Distributed Algorithms for Large-scale Machine Learning

CS Faculty Candidate Talk: Dr. Xinwei Zhang

Title: The Art of Designing Distributed Algorithms for Large-scale Machine Learning

Time: February 25, 2025 at 10:00 AM Eastern Time (US and Canada)

Location: Healy Library 10th floor seminar room 0025E Refreshments will be served.

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Abstract: Distributed algorithms are fundamental to modern applications in machine learning, signal processing, and control systems. However, designing and analyzing these algorithms remains challenging due to the intricate interplay of communication, computation, and scalability.

Bio: Xinwei Zhang is a Postdoctoral Fellow in Prof. Meisam Razaviyayn’s group, in the Department of Industrial and System Engineering at University of Southern California. He received Ph.D. and M.S. degree in Electrical Engineering at the University of Minnesota advised by Prof. Mingyi Hong and Sairaj Dhople in 2023 and 2022, respectively. He received his B.S. degree in Automation at University of Science and Technology of China in 2018. His research focuses on contemporary issues in differential privacy and distributed optimization, including differential privacy for large-scale training machine learning models, the theoretical aspect of federated learning, decentralized optimization, and distributed machine learning system design. His broad research interest lies at the intersection of machine learning, signal processing, and control theory. He has published over 20 papers in conferences and journals, including ICML, ICLR, NeurIPS, IEEE TSP, IEEE SPM, and SIOPT.