Dr. Zhangyang “Atlas” Wang
CV
News
New York
Since May 2024, Dr. Wang has been on leave from UT Austin to serve as the full-time Research Director for XTX Markets.
Read MoreEducation
University of Illinois Urbana-Champaign (UIUC)
Ph.D. in Electrical and Computer Engineering
advised by Prof. Thomas S. Huang
University of Science and Technology of China (USTC)
B.E. in Electrical Engineering and Information Science
Professional Experience
Research Director (Full Time)
XTX Markets (found & lead the new XTY AI Labs)
Temple Foundation Endowed Associate Professor #7
(tenured & currently on leave at XTX Markets)
Jack Kilby/Texas Instrument Endowed Assistant Professor
Dept. Electrical and Computer Engineering
Dept. Computer Science (GSC Faculty)
Oden Institute (Affiliated Faculty)
NSF IFML AI Institute & UT Machine Learning Lab (Core Member)
UT Center for Generative AI (Area Leader)
UT Center for Autonomy (Core Member)
UT InfraAI Center (Core Member)
The University of Texas at Austin, TX
Director of AI Research (Part Time)
Picsart, Inc
(led the company's video GenAI initiative)
Amazon Visiting Academic (Part Time)
Amazon Search
(geometric DL for recommendation systems)
Assistant Professor
Dept. Computer Science and Engineering
Texas A&M University, College Station, TX
Research Intern
Microsoft Research, Redmond, WA
(distributed ML training)
Research Intern
Adobe Research, San Jose, CA
(creative visual editing)
Research Intern
US Army Research Laboratory, Adelphi, MD
(ML for geo-mapping)
Research Interests
I pursue a low-dimensional lens on intelligence to uncover the foundational blueprints of scalable and generalizable AI. This philosophical view has been driving my research ventures across language, vision, and other domains.
My recent work focuses on:
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Training dynamics that inherently discover low-dimensional inductive biases, e.g, leveraging sparsity, low-rank manifolds, algebraic symmetries and invariants, to move beyond brute-force optimization in pre-training, SFT, and RL
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Test-time behaviors where these structures manifest as efficiently self-organized, composite mechanisms for inference, reasoning, and agentic planning
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Real-world deployment where these structural invariants translate into resource-aware and trustworthy AI for high-stakes interdisciplinary domains such as robotics, medicine, and finance
Honors and Impact
Faculty Research Awards
Best Paper Runner-Up, NeurIPS 2026 Linguistic Principles for Foundation Models Workshop
J. K. Aggarwal Prize, IAPR
Best Paper Honorable Mention, ICML 2026 AI for Math workshop
Best Paper Finalist, AAMAS 2026
Best Paper Award, ACM MM 2025 Multimodal Foundation Models for Spatial Intelligence workshop
Best Paper Award, CVPR 2025 AI for Content Creation workshop
Outstanding Paper (Honorable Mention), MLSys 2025
DAPRA Disruptive Idea Paper Award, NeuS 2025
Sony Faculty Research Award
IEEE Signal Processing Society Young Author Best Paper Award
AI 100: The Top AI Thought Leaders (by H2O.ai)
Best Paper Finalist, VLDB 2024
IEEE AI's 10 to Watch
Google Research Scholar Award
Best Paper Award, Learning on Graphs (LoG) Conference 2022
Meta Reality Labs Research Award
NSF CAREER Award
INNS Aharon Katzir Young Investigator Award
J. P. Morgan AI Faculty Research Award
Google TensorFlow Model Garden Award
Adobe Data Science Research Award
ARO Young Investigator Award (YIP)
Amazon Research Award
IBM Faculty Research Award
TEES Young Faculty Fellow, TAMU
Engineering Genesis Award for Multidisciplinary Research, TAMU
Lenovo AI Innovation Challenge Award
Selected Media Coverage
Nature
"Too Much Social Media Gives AI Chatbots Brain Rot" (Oct 2025)
WIRED
"AI Models Get Brain Rot, Too" (Oct 2025)
Fortune
Forbes
Quanta Magazine
"Will Transformers Take Over Artificial Intelligence?" (Mar 2022)
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Our research paper covered in this article, TransGAN (NeurIPS'21), has also been highlighted by top AI influencers such as Andrew Ng and Yannic Kilcher
NSF News
MIT News
"Shrinking massive neural networks used to model language" (Dec 2020)
"Learning to Grow Machine-learning Models" (Mar 2023)
IEEE Intelligent Systems
"AI's 10 to Watch, 2022" (Apr 2023)
IEEE Computer Magazine
UT Engineering News
IDG Connect
"Reducing energy use in neural networks" (Jan 2021)
US ARL's "10 Coolest Techniques"
Our work on vision-based decentralized robotic control (VGAI) was selected as No. 3 of Year 2020. [Video Demo] (Oct 2020)
International Business Times
"Adobe Photoshop unveils artificial intelligence
tool to identify fonts from 20,000 typefaces" (May 2016)
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Also in: BBC News (Apr 2016)
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Fortune (Apr 2016)
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UIUC Alumni Magazine (Spring 2016 edition)
Research Funds
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Gratefully supported by NSF (10 grants), DARPA (5), ARL (3), IARPA (2), ARO (2), DOE (1), in addition to over two dozen of industry and university grants.
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As of March 2025, the total project funding amounts to $35 million, and PI Wang's personal share of external funding exceeds $8.75 million.
Other Awards (won myself as student)
Nov 2015
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Baidu Scholarship (10 worldwide annually)
May 2016
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Graduate College Dissertation Fellowship, UIUC
Apr 2016
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Thomas and Margaret Huang Award for Graduate Research, UIUC
May 2015
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Cognitive Science & AI Research Award, UIUC
Services
Conference Services
Program Chair
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International Conference on Automated Machine Learning (AutoML), 2026
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Conference on Parsimony and Learning (CPAL), 2024 (inaugural & founding chair)
Senior Area Chair
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NeurIPS (2024 - 2026), ICML (2026), CVPR (2027)
Area Chair
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ICLR (2022 - 2027), ICML (2022 - 2025), NeurIPS (2022, 2023)
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CVPR (2022 - 2026), ICCV (2023), ECCV (2024)
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AISTATS (2025, 2026), AAAI (2021 - 2024), DAC (2023, 2024)
Recognized as:
NeurIPS 2023 Notable Area Chair
ICLR 2022 Highlighted Area Chair
NeurIPS 2021 & ICLR 2021 Outstanding Reviewer
Outstanding Paper Award Committee
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ICLR (2022)
Others
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Tutorial Chair, CVPR (2027)
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AI/ML Track Chair, ICPR (2026)
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Knowledge Graph Day Chair, WWW (2023)
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Publicity Chair, ICME (2023)
Journal Editorial Board
2025 - present
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Journal of Articial Intelligence Research (JAIR)
2024 - present
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Transactions on Machine Learning Research (TMLR)
Professional Society
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IEEE Senior Member
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ACM Member (ACM Distinguished Speaker)
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Mentor, IEEE SPS Underrepresented Young Researchers Program (ME-UYR)
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Member, IEEE ML for Signal Processing Technical Committee (MLSP TC)