Dr. Tianyun Zhang received his Ph.D. in electrical and computer engineering from Syracuse University in 2021. His research interests include model compression and hardware acceleration for artificial intelligence systems, adversarial machine learning and robust learning, and efficient machine learning. His research papers have been published in competitive conferences such as NeurIPS, ECCV, ICCV, AAAI, ICDM, ASPLOS, DAC, as well as high-impact journals such as IEEE Transactions on Neural Networks and Learning Systems (TNNLS) and Neurocomputing.

Dr. Zhang employs many mathematical optimization techniques in the research of deep learning. He proposes to solve the weight pruning problem on deep neural networks using alternating direction method of multipliers (ADMM). He also proposes a unified min-max optimization framework for robust learning over multiple domains. The ADMM-based weight pruning framework for deep neural networks, proposed in his work, is one of the state-of-the-art model compression methods. 

In his spare time, he likes many sports and exercises, such as basketball and hiking.