Professor Zhangyang “Atlas” Wang 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, and the Oden Institute CSEM program. Since May 2024, Dr. Wang has been on leave from UT Austin to serve as the full-time Research Director for XTX Markets, heading the newly established AI Lab in New York City. In this role, he leads groundbreaking efforts at the intersection of algorithmic trading and deep learning, driving the development of robust, scalable AI algorithms to extract predictive insights from massive datasets.

Previously, he was the Jack Kilby/Texas Instruments Endowed Assistant Professor in the same department from 2020 to 2023; and an Assistant Professor of Computer Science and Engineering at Texas A&M University from 2017 to 2020. Alongside his academic career, he has also explored multiple exciting opportunities in the industry. He was a visiting scholar at Amazon Search from 2021 to 2022, leveraging geometric deep learning for recommendation systems. Later, he took on the (part-time) role of Director of AI Research & Technology for Picsart from 2022 to 2024, where he led the company’s ambitious initiative in video generative AI. He earned his Ph.D. in Electrical and Computer Engineering from UIUC in 2016, under the guidance of Professor Thomas S. Huang, and his B.E. in EEIS from USTC in 2012.

Prof. Wang has broad research interests spanning from the theory to the application aspects of machine learning (ML) and optimization. Currently, his research passion centers on developing the theoretical and algorithmic foundations of generative AI and neurosymbolic AI. He emphasizes low-dimensional, modular representations that enable efficient and reliable learning while bridging the gap to symbolic reasoning over discrete structures such as logical dependencies, causal relationships, and geometric invariants. These principles underpin efforts to enhance the efficiency and trustworthiness of large language models (LLMs), advance planning and reasoning capabilities, and foster innovations in generative vision. 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 served as its inaugural Program Chair. He regularly serves as (senior) 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 AI 100 Top Thought Leader 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 at the inaugural Learning on Graphs (LoG) Conference 2022, the Best Paper Finalist Award at the International Conference on Very Large Databases (VLDB) 2024, and five research competition prizes at 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 eight prestigious fellowships (NSF GRFP, Apple, NVIDIA, Adobe, IBM, Amazon, Qualcomm, and Snap), among many other honors.