About me
Iām a fourth year Ph.D. student in Applied Mathematics at Stony Brook University supervised by Professor Yi Liu.
Previously, I obtained a Bachelor of Science degree from Stony Brook University in both applied mathematicsš and pure mathematicsš. During my undergraduate years, I was fortunate to conduct research under the guidance of Dr. Chunmei Wang (Department of Mathematics, University of Florida) and Dr. Haizhao Yang (Department of Mathematics, University of Maryland).
My research focuses on Geometric Deep Learning, Generative Models, Neural Operators, and AI for Science in general.
The best way to contact me is through email: wenhan(dot)gao(at symbol)stonybrook(dot)edu š«
Education
- Computer Science and Information Security (3.97/4.0); Queensborough Community College, Aug. 2018 - May 2019
- B.S. Mathematics; Applied Mathematics & Statistics (3.97/4.0); Stony Brook University, Aug. 2019 - Dec. 2021
- Ph.D. Operations Research (4.0/4.0); Stony Brook University, Aug. 2022 - TBD
News
- October 2025: Gave a talk at the Institute for Advanced Computational Science (IACS-Stony Brook) on AI for Science. Abstract;Slides
- May 2025: Co-first author on a paper accepted by ICML 2025 on 3D GNN explanation (XAI)!
- April 2025: First author on a paper accepted by TMLR on expressive neural operators!
- January 2025: First author on a paper accepted by ICLR 2025 on discretization mismatch errors in neural operators.
- December 2024: Accepted an offer from Visa Research to work as an intern staff research scientist over the summer.
- November 2024: Passed the preliminary exam in AMS. Slides
- October 2024: Received the NeurIPS Scholar Award, see you in Vancouver.
- September 2024: Co-first author on a paper, in collaboration with FSU, accepted by NeurIPS on symmetries for molecular GNN active learning!
- September 2024: First author on a paper accepted by TMLR on symmetries in neural operators!
- August 2024: Finished teaching AMS 326, Numerical Analysis. Congrats to all my students on a job well done! Syllabus;Lecture Notes
- August 2024: Mentored undergraduate student Xiang Liu (freshman in CS/AMS), who successfully completed a summer research project on neural operators for climate change through the SUNY SOAR program. Congrats!
- March 2024: Awarded the Excellence in Student Teaching Award from the AMS department at Stony Brook for Fall 2023!
- December 2023: Finished teaching the graduate course AMS 595, Fundamentals of Computing. Congrats to all my students on a job well done! Syllabus;Lecture Notes
- August 2023: Passed Ph.D. qualifying exam in AMS!
- February 2023: First author on a paper on active learning-based sampling for high-dimensional PDEs published in the Journal of Computational Physics.
First or Co-first Author Publications
* indicates equal contributors.
RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation
J.Qu*, W.Gao*, J.Zhang, X.Liu, H.Wei, H.Ling, Y.Liu.
The Forty-second International Conference on Machine Learning (ICML), 2025
Dynamic Schwartz-Fourier Neural Operator for Enhanced Expressive Power
W.Gao, J.Luo, R.Xu, Y.Liu.
Transactions on Machine Learning Research (TMLR), 2025
Discretization-invariance? On the Discretization Mismatch Errors in Neural Operators
W.Gao, R.Xu, Y.Deng, Y.Liu.
The Thirteenth International Conference on Learning Representations (ICLR), 2025
Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism
R.Subedi*, L.Wei, W.Gao*, S.Chakraborty, Y.Liu.
The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
Coordinate Transform Fourier Neural Operators for Symmetries in Physical Modelings
W.Gao, R.Xu, H.Wang, Y.Liu.
Transactions on Machine Learning Research (TMLR), 2024
Active Learning Based Sampling for High-Dimensional Nonlinear Partial Differential Equations
W.Gao, C.Wang.
Journal of Computational Physics (JCP), 2023
First or Co-first Author Preprints
- One paper on LLM-assisted drug editing; Submitted to ICLR 2026; W.Gao el al.
- One paper on size-consistent diffusion models for 3D molecular generation; Submitted to ICLR 2026; W.Gao, J.Qu, Y.Liu
- One paper that reveals neural operators can learn hidden physics from data; In Submission to ICML 2026; W.Gao*, J.Luo*, R.Xu, F.Wan, X.Liu, Y.Liu
- One paper addressing the misalignment between optimized masks and actual explanatory subgraphs in 3D GNNs; Submitted to TPAMI; X.Liu*, W.Gao*, Y.Liu