Assistant Professor

photo of Hongyang ZhangDr. Hongyang Zhang will be joining the Cheriton School of Computer Science as a tenure-track Assistant Professor in July 2021.

Dr. Zhang’s research is on the study of robust, secure, and trustworthy machine learning. As machine learning tools become increasingly popular in real applications, there is a serious threat that such technological innovations may cause inadvertent or even malicious harm to certain groups or the population at large. Indeed, intensive recent work on this topic suggests that machine learning models are vulnerable to adversarial attacks, outliers, and various forms of noise. Nonetheless, Dr. Zhang as well as some others have shown that it is possible to design new learning algorithms that are provably and empirically robust to these corruptions with record-breaking performance.

Besides making solid theoretical contributions, Dr. Zhang also emphasizes combining theory with practice in his research. For example, his theoretically principled algorithm TRADES has been featured by various third-party benchmarks / leaderboards and won the championship in the NeurIPS’18 Adversarial Vision Challenge, in which it beat hundreds of teams from industry. He also won the championship out of 1,558 teams in the CVPR 2021 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet.

Dr. Zhang publishes regularly in the top venues for machine learning (NeurIPS, ICML, COLT, AAAI and JMLR) and in other top venues specialized on computer vision, such as ECCV and a monograph titled “Low Rank Models for Visual Analysis: Theories, Algorithms and Applications,” as well as top venues in algorithms and complexity, such as SODA, ICALP and ITCS.

As of May 2021, Dr. Zhang’s publications have been collectively cited more than 1,180 times with an h-index of 17 from Google Scholar.

Dr. Zhang is currently a postdoctoral fellow at the Toyota Technological Institute at Chicago, working with Avrim Blum and Greg Shakhnarovich. He has a PhD (2019) and a Master’s (2018) in machine learning from Carnegie Mellon University. Prior to that, he graduated from Peking University (2015) in Computer Science.

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