Sanghyun Hong

Sanghyun Hong is a Ph.D. candidate in Computer Science at the University of Maryland, College Park (UMD), advised by Professor Tudor Dumitras. His research interests span the security and privacy of machine learning (ML). In his dissertation research, he exposed the vulnerability of deep learning algorithms to hardware attack vectors, such as Rowhammer or side-channel attacks. He also worked on identifying hidden properties within deep learning algorithms, such as overthinking and gradient-level disparity, whose quantification led to defensive mechanisms against backdoor or data poisoning attacks, respectively. His research outcomes are published in security and ML conferences: USENIX, ICLR, ICML, and NeurIPs. He is a recipient of the Ann G. Wylie Dissertation Fellowship and is currently a Future Faculty Fellow in A. James Clark School of Engineering at UMD. He is on the academic job market this year.