University of Texas at Austin
Atlas Wang




phone (512) 471-1866

office EER 6.886

Atlas Wang

GSC Faculty Affiliated faculty (non-Core)

Assistant Professor Electrical & Computer Engineering

Centers and Groups

Research Interests

Machine Learning Optimization


Professor Zhangyang “Atlas” Wang is currently an Assistant Professor of Electrical and Computer Engineering at UT Austin. He was an Assistant Professor of Computer Science and Engineering at Texas A&M University from 2017 to 2020. 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 is broadly interested in the fields of machine learning, computer vision, optimization, and their interdisciplinary applications. His latest interests focus on automated machine learning (AutoML), learning-based optimization, machine learning robustness, and efficient deep learning. His research is gratefully supported by NSF, DARPA, ARL/ARO, as well as a few more industry and university grants. He is an elected technical committee member of IEEE MLSP; an associate editor of IEEE TCSVT (in which capacity he received the 2020 IEEE TCSVT 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 ARO Young Investigator award, an IBM Faculty Research Award, an Amazon Research Award (AWS AI), an Adobe Data Science Research Award, a Young Faculty Fellow of TAMU, and four research competition prizes from CVPR/ICCV/ECCV.

Research interests: 
[A] As Goals -- Enhancing Deep Learning Robustness, Efficiency, and Privacy 
Seek to build deep learning solutions that are way beyond just data-driven accurate predictors. In his opinion, an ideal model shall at least: (1) be robust to perturbations and attacks (therefore trustworthy); (2) be efficient and hardware-friendly (for deployments in practical platforms); and (3) be designed to respect individual privacy and fairness. 
[B] As Toolkits -- Automated Machine Learning (AutoML), and Learning-Augmented Optimization 
Is enthusiastic about the rising field of AutoML, on both consolidating its theoretical underpinnings and broadening its practical applicability. State-of-the-art ML systems consist of complex pipelines, with choices of model architectures, algorithms and hyperparameters, as well as other configuration details to be tuned for optimal performance. They further often need to be co-designed with multiple goals and constraints. He considers AutoML to be a powerful tool and a central hub, in addressing those design challenges faster and better.
 [C] As Applications -- Computer Vision and Interdisciplinary Problems 
Interested in a broad range of computer vision problems, ranging from low-level (e.g, image reconstruction, enhancement and synthesis) to high-level topics (e.g., recognition, segmentation, and vision for UAV/autonomous driving). He is also growingly interested in several interdisciplinary fields, such as biomedical informatics, geoscience, and IoT.