Regular biography
Quanquan Gu is an Associate Professor in the Department of Computer Science at the University of California, Los Angeles. His research interests include machine learning, data mining, and optimization algorithms, with a focus on computational genomics. Gu's work has been published in leading conferences and journals, including NeurIPS, ICML, and JMLR. His research explores topics such as nonconvex optimization, distributed learning, and privacy-preserving methods. For more information, visit his website at https://samueli.ucla.edu/people/quanquan-gu/ or contact him at qgu@cs.ucla.edu.
Scholar-generated biography
Quanquan Gu is an Associate Professor of Computer Science at UCLA, specializing in AGI, Large Language Models, Reinforcement Learning, and Nonconvex Optimization. His research explores the theoretical foundations and practical applications of these areas, including improving adversarial robustness, understanding the spectral bias of deep learning, and analyzing the convergence of optimization algorithms. Gu's work also addresses challenges in personalized recommendation systems, neural contextual bandits, and the generalization of deep neural networks. His contributions span both theoretical analysis and empirical evaluation, with a focus on advancing the capabilities and reliability of machine learning systems.