Jiajin Li

Jiajin Li is an Assistant Professor in the Operations and Logistics Division at The University of British Columbia Sauder School of Business. His research interests include Continuous Optimization, Design and Analysis of Optimization Algorithms, and Machine Learning. Li holds a BSc from Chongqing University and a PhD from The Chinese University of Hong Kong. His work has been published in top conferences and journals, including ICML, NeurIPS, and IEEE Signal Processing Magazine. He teaches courses in Logistics and Operations Management, Seminar on Theoretical Developments in Management, and Optimization Theory and Applications.

Jiajin Li is a researcher at The University of British Columbia, specializing in Optimization and Machine Learning. Their work focuses on advancing methods for anomaly detection, robust statistics, and graph-based learning. Li's research explores the intersection of optimization techniques with machine learning, particularly in areas such as graph neural networks, variational autoencoders, and distributionally robust optimization. They have contributed to the development of algorithms for nonconvex-nonconcave minimax optimization, optimal transport, and robust graph alignment. Their publications highlight the application of mathematical frameworks to improve the reliability and performance of machine learning models in complex and noisy environments.