Professor Zhangyang "Atlas" Wang is currently an Assistant Professor and Fellow of the Jack Kilby/Texas Instruments Endowed Faculty Fellowship in Computer Engineering in the Department of Electrical and Computer Engineering at The University of Texas at Austin.
During 2021 - 2022, he held a visiting researcher position at Amazon Search. From 2017 to 2020, he was an Assistant Professor of Computer Science and Engineering, at the Texas A&M University. He received his Ph.D. degree in ECE from UIUC in 2016, advised by Professor Thomas S. Huang; and his B.E. degree in EEIS from USTC in 2012. Prof. Wang has broad research interests spanning from the theory to the application aspects of machine learning (ML). Most recently, he studies efficient ML / learning with sparsity, robust & trustworthy ML, AutoML / learning to optimize (L2O), and graph ML, as well as their applications in computer vision and interdisciplinary science.
His research is gratefully supported by NSF, DARPA, ARL, ARO, IARPA, DOE, as well as dozens of industry and university grants. He is/was an elected technical committee member of IEEE MLSP and IEEE CI; an associate editor of IEEE TCSVT (receiving the 2020 Best Associate Editor Award); and frequently serves as area chairs, guest editors, invited speakers, various panelist positions and reviewers. He has received many research awards and scholarships, including most recently an NSF CAREER Award, an ARO Young Investigator Award, an INNS Aharon Katzir Young Investigator Award, an IBM Faculty Research Award, a J. P. Morgan Faculty Research Award, an Amazon Research Award, an Adobe Data Science Research Award, a Google TensorFlow Model Garden Award, a Young Faculty Fellow of TAMU, and five research competition prizes from CVPR/ICCV/ECCV.