Farzan Farnia
B.Sc. (Sharif University of Technology), M.Sc. and Ph.D. (Stanford University)
Assistant Professor



Machine Learning, Optimization, Information Theory






SHB 918
+852 3943 1279
farnia[@]cse.cuhk.edu.hk

Research Areas

Artificial Intelligence Deep Learning
Machine Learning
Computer Theory Optimization
Theory and Algorithms

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Biography
Farzan Farnia is an assistant professor at the Department of Computer Science and Engineering at the Chinese University of Hong Kong. Prior to joining CUHK, he was a postdoctoral research associate at the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, from 2019-2021. He received his M.Sc. and Ph.D. degrees in electrical engineering from Stanford University and his B.Sc. degree in electrical engineering and mathematics from Sharif University of Technology. At Stanford, he was a graduate research assistant at the Information Systems Laboratory advised by David Tse. Farzan’s research interests include machine learning, deep learning theory, optimization, and information theory. He has been the recipient of a Stanford Graduate Fellowship from 2013-2016 and the Numerical Technology Founders Prize as the second top performer of Stanford Electrical Engineering Ph.D. Qualifying Exams in 2014.



Top Cited Publications
Farzan Farnia, Asuman Ozdaglar: Do GANs always have Nash equilibria? ICML 2020
Farzan Farnia, Jesse Zhang, David Tse: Generalizable Adversarial Training via Spectral Normalization. ICLR 2019
Soheil Feizi, Farzan Farnia, Tony Ginart, David Tse: Understanding GANs in the LQG setting: Formulation, Generalization and Stability. IEEE Journal on Selected Areas in Information Theory 2020
Farzan Farnia, David Tse: A Convex Duality Framework for GANs. NeurIPS 2018
Farzan Farnia, David Tse: A Minimax Approach to Supervised Learning. NeurIPS 2016


Recent Publications
Farzan Farnia, Asuman Ozdaglar: Train simultaneously, generalize better: Stability of gradient-based minimax learners. ICML 2021
Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, Ali Jadbabaie: Robust Federated Learning: The Case of Affine Distribution Shifts. NeurIPS 2020
Farzan Farnia, William Wang, Subhro Das, Ali Jadbabaie: GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models.


Achievements and Awards
Awarded Numerical Technology Founders Prize, 2014
Second Top Performer of Stanford Electrical Engineering Ph.D. Qualifying Exams, 2014
Awarded Stanford Graduate Fellowship, 2013