Professor Zhangyang “Atlas” Wang [Google Scholar] is a tenured Associate Professor and holds the Temple Foundation Endowed Faculty Fellowship #7, in the Chandra Family Department of Electrical and Computer Engineering at The University of Texas at Austin. He is also a faculty member of UT Computer Science (GSC) [CSRankings], and the Oden Institute CSEM program. Meanwhile, in a part-time role, he serves as the Director of AI Research & Technology for Picsart, where he leads the development of cutting-edge, GenAI-powered tools for creative visual editing. He was the Jack Kilby/Texas Instruments Endowed Assistant Professor in the same department from 2020 to 2023. From 2017 to 2020, he was an Assistant Professor of Computer Science and Engineering, at the Texas A&M University. During 2021 - 2022, he also held a visiting researcher position at Amazon Search. 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). At present, his core research mission is to leverage, understand and expand the role of sparsity, from classical optimization to modern neural networks, whose impacts span over many important topics such as efficient training/inference/transfer (especially, of large foundation models), robustness and trustworthiness, learning to optimize (L2O), generative AI, and graph learning. His research is gratefully supported by NSF, DARPA, ARL, ARO, IARPA, DOE, as well as dozens of industry and university grants. Prof. Wang co-founded the new Conference on Parsimony and Learning (CPAL) and serves as its inaugural Program Chair. He is an elected technical committee member of IEEE MLSP and IEEE CI; and regularly serves as area chairs, invited speakers, tutorial/workshop organizers, various panelist positions and reviewers. He is an ACM Distinguished Speaker and an IEEE senior member.
Prof. Wang has received many research awards, including an NSF CAREER Award, an ARO Young Investigator Award, an IEEE AI's 10 To Watch Award, an INNS Aharon Katzir Young Investigator Award, a Google Research Scholar award, an IBM Faculty Research Award, a J. P. Morgan Faculty Research Award, an Amazon Research Award, an Adobe Data Science Research Award, a Meta Reality Labs Research Award, and two Google TensorFlow Model Garden Awards. His team has won the Best Paper Award from the inaugural Learning on Graphs (LoG) Conference 2022; and has also won five research competition prizes from CVPR/ICCV/ECCV since 2018. He feels most proud of being surrounded by some of the world's most brilliant students: his Ph.D. students include winners of seven prestigious fellowships (NSF GRFP, IBM, Apple, Adobe, Amazon, Qualcomm, and Snap), among many other honors.
At VITA group, we have unusually broad, and forever-evolving research interests spanning from the theory to the application aspects of machine learning (ML). Our current "research keywords" include, but are not limited to: sparsity (from classical optimization to modern neural networks); efficient training, inference or transfer (especially, of large foundation models); robustness and trustworthiness; learning to optimize (L2O); generative AI; graph learning, and more. Below, we describe a few organized themes that are driving our group's latest efforts.
Substantial efforts have been devoted to scaling deep NNs to enormous sizes, with the time and financial outlay necessary to train these models growing in concert. Sparse NNs, whose large portions of parameters (or activations, gradients, etc.) are zero, have been promising to address the enlarging gap between model scale and resource budget. Early approaches first train dense NNs and then prune the trained NNs. Those methods significantly reduce the inference complexity yet cost even greater computational resources and memory footprints at training. We have instead explored the fresh prospect of directly training smaller, sparse NNs in place of the large dense models without sacrificing task performance. Our group has contributed many well-recognized works to laying theoretical foundations for sparse NNs’ efficiency, optimization, and generalization; and to demonstrating their empirical promise in both TinyML and large foundation model (e.g. LLM) applications. We also created a short handbook for sparse NN researchers.Selected Notable Works:
Our group's earlier (pre-2021) work includes several influential algorithms for image enhancement and editing “in the wild,” many of which are based on Generative Adversarial Networks (GANs). We pioneered a few innovative GAN architectural designs (TransGAN, DeblurGAN-v2, EnlightenGAN, AutoGAN) that are now widely adopted by the community. More recently (post-2021), we have steered our focus to two new areas: (i) 3D reconstruction and novel view synthesis, via Neural Radiance Fields (NeRF); (2) the new generation of multi-modality GenAI, leveraging the latest workhorse of diffusion models (text2image, text2video, text-to-3D, etc.)Selected Notable Works:
L2O is an emerging paradigm that leverages ML to automatically develop an optimization algorithm. It demonstrates many practical benefits including faster convergence and better solution quality. Over the past five years, we have spearheaded an ever-growing line of L2O works that significantly expand both rigorous theories (L2O convergence, worst-case/average-case generalization, adaptation, uncertainty quantification, and interpretability), and practical adoption (inverse problems in computational sensing/imaging, large model training, private training, protein docking, AI for finance, among others). Please refer to the L2O Primer and Open L2O toolbox that we presented for this community.Selected Notable Works:
We are devoted to studying emerging model families that promise to become future “universal” workhorses or "foundational models": two such examples are transformers (especially LLMs) and graph neural networks, and many projects here are owing to our close collaboration with industry leaders. We are meanwhile enthusiastic about AutoML & neural scaling law, on both consolidating its theoretical underpinnings ("why choosing this model, not that one?") and broadening its practical applicability ("what more can be automated, and how to do it better?"). State-of-the-art ML systems consist of complex pipelines with multiplied design choices. We see AutoML as a central hub in addressing those design challenges; it also proves to be a powerful tool for understanding many ad-hoc choices of network architectures or hyperparameters (often aided by the deep learning theory).Selected Notable Works:
As ML systems (in particular, computer vision and LLM) are influencing all facets of our daily life, it is now commonplace to see evidence on the untrustworthiness or harmful impacts of ML systems in high-stake environments. We have strived to build ML algorithms that are resilient to various environment degradations, perturbations, adversarial attacks, and privacy threats - as overviewed in our ML Safety Primer. We are also keen on developing AI4sicnece (protein, medical image, material science), and AI for the Common Good (our Good Systems project)Selected Notable Works: