Yu Guang Wang - 上海交通大学 - 自然科学研究院

个人简介

I am an Associate Professor in Institute of Natural Sciences , School of Mathematical Sciences , Department of Computer Science and Engineering , and Key Lab of Scientific and Engineering Computing of Minister of Education (MOE-LSC) , at Shanghai Jiao Tong University. I am Adjunct Associate Professor at Shanghai AI Laboratory and UNSW Sydney . My research interests lie in artificial intelligence, computational mathematics, statistics and data science. In particular, I am working on geometric deep learning, graph neural networks, applied harmonic analysis, Bayesian inference, information geometry, numerical analysis, and applications to biomedicine and protein design. Previously, I was a research scientist at Max Planck Institute for Mathematics in Sciences, in Prof Guido Montufar's Deep Learning Theory Group . I obtained my PhD in applied mathematics from University of New South Wales under supervision of Prof Ian Sloan and Rob Womersley . I am a recipient of ICERM Semester Postdoctoral Fellowship of Brown University (2018), a long-term IPAM visitor of UCLA (2019), and long-term visitor of AI Group of Prof Pietro Lio at Univeristy of Cambridge (2022).

研究领域

Artificial Intelligence: Graph Neural Networks , Geometric Deep Learning , Large Language Model , Information Geometry , Optimization Algorithms , Distributed Learning , Generative Models , Topological Data Analysis , Expressivity and Generalization of Deep Neural Networks , Kernel Methods , Reinforcement Learning Applied Math and Statistics: Applied Harmonic Analysis , Signal Processing , Bayesian Statistics , Computational Geometry , SDEs Interdisciplinary: AI for Sciences , AI for Mathematics , AI for Health/Medicine , AI for Synbio and Protein Design , Cosmic Microwave Background (CMB)

近期论文

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1. Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system. R. Gao, X. Yuan, Y. Ma, T. Wei, L. Johnston, Y. Shao, W. Lv, T. Zhu, Y. Zhang, J. Zheng, G. Chen, J. Sun, Y. G. Wang, Z. Yu. Cell Reports Medicine 2024 . 2. Graph Denoising Diffusion for Inverse Protein Folding. Y. Kai, B. Zhou, Y. Shen, P. Lio, Y. G. Wang. NeurIPS 2023 . 3. EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning. C. Xu, R. T. Tan, Y. Tan, S. Chen, Y. G. Wang, X. Wang, Y. Wang. CVPR 2023 . 4. How powerful are shallow neural networks with bandlimited random weights? M. Li, S. Sho, F. Cao, Y. G. Wang, J. Liang. ICML 2023 . 5. Robust Graph Representation Learning for Local Corruption Recovery. B. Zhou, Y. Jiang, Y. G. Wang, J. Liang, J. Gao, S. Pan, X. Zhang. WWW 2023 (Also in ICML 2022 Workshop on Topology, Algebra, and Geometry in Machine Learning) . 6. ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks. Y. Wang, K. Yi, X. Liu, Y. G. Wang, S. Jin. ICLR 2023 (Spotlight) (Also in NeurIPS 2022 Workshop on New Frontiers in Graph Learning) 7. Lightweight Equivariant Graph Representation Learning for Protein Engineering. B. Zhou, O. Lv, K. Yi, X. Xiong, L. Hong, Y. G. Wang. NeurIPS 2022 Workshop on Machine Learning in Structral Biology 8. Cell Graph Neural Networks Enable Digital Staging of Tumour Microenvironment and Precisely Predict Patient Survival in Gastric Cancer. Y. Wang, Y. G. Wang, C. Hu, M. Li, Y. Fan, N. Otter, I. Sam, H. Gou, Y. Hu, T. Kwok, J. Zalcberg, A. Boussioutas, R. J. Daly, G. Montufar, P. Lio, D. Xu, G. I. Webb, J. Song. npj Precision Oncology 2022 . 9. Weisfeiler and Lehman Go Cellular: CW Networks. C. Bodnar, F. Frasca, N. Otter, Y. G. Wang, P. Lio, G. Montufar, M. Bronstein. NeurIPS 2021 . 10. Distributed Learning via Filtered Hyperinterpolation on Manifolds. G. Montufar, Y. G. Wang. Foundations of Computational Mathematics 2021 . 11. How Framelets Enhance Graph Neural Networks. X. Zheng, B. Zhou, J. Gao, Y. G. Wang, P. Lio, M. Li, G. Montufar. ICML 2021 (Spotlight) . Code 12. Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. C. Bodnar, F. Frasca, Y. G. Wang, N. Otter, G. Montufar, P. Lio, M. Bronstein. ICML 2021 (Spotlight) . (Also as a Spotlight in ICLR 2021 Workshop on GTRL) . 13. Decimated Framelet System on Graphs and Fast G-Framelet Transforms. X. Zheng, B. Zhou, Y. G. Wang, X. Zhuang. Journal of Machine Learning Research 2022. Code 14. Algorithm 1018: FaVeST-Fast Vector Spherical Harmonic Transforms. M. Li, Q. T. Le Gia, Y. G. Wang. ACM Transactions on Mathematical Software 2021 . Code also in CALGO (see file 1018.gz) 15. Improve Concentration of Frequency and Time by Novel Complex Spherical Designs. M. Sourisseau, Y. G. Wang, R. S. Womersley, H.-T. Wu, W.-H. Yu. Applied and Computational Harmonic Analysis 2021 . Code 16. Distributed Filtered Hyperinterpolation For Noisy Data on the Sphere. S.-B. Lin, Y. G. Wang, D.-X. Zhou. SIAM Journal on Numerical Analysis 2021 . 17. Path Integral Based Convolution and Pooling for Graph Neural Networks. Z. Ma, J. Xuan, Y. G. Wang, M. Li, P. Lio. NeurIPS 2020 . Code 18. Haar Graph Pooling. Y. G. Wang, M. Li, Z. Ma, G. Montufar, X. Zhuang, Y. Fan. ICML 2020 . Code 19. Tight Framelets and Fast Framelet Filter Bank Transforms on Manifolds. Y. G. Wang, X. Zhuang. Applied and Computational Harmonic Analysis , 48 (1): 64–95, 2020. 20. Isotropic Sparse Regularization for Spherical Harmonic Representations of Random Fields on the Sphere. Q. T. Le Gia, I. H. Sloan, R. S. Womersley, Y. G. Wang. Applied and Computational Harmonic Analysis , 49 (1): 257–278, 2020. 21. Fully Discrete Needlet Approximation on the Sphere. Y. G. Wang, Quoc T. Le Gia, I. H. Sloan, R. S. Womersley. Applied and Computational Harmonic Analysis , 43 : 292–316, 2017. Code

学术兼职

Guest Associate Editor for Special Issue Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications in IEEE Transactions on Neural Networks and Learning Systems (Top AI Journal). Call for Papers! Review Editor for the journal Frontiers in Applied Mathematics and Statistics . Reviewer for ICML'20 (Top Reviewer), ICML'20-23 , NeurIPS'21-23, NeurIPS'21 , ICLR'21-23 , IJCAI'21-23 . Organiser for Collaborate@ICERM on Geometry of Data and Networks , 2019 joint with Joan Bruna (NYU) , Zheng Ma (Princeton) , Guido Montútar (UCLA) , Nina Otter (UCLA) . Organiser for Minisymposium on Harmonic Analysis for Graph Signal Processing and Deep Learning Applications in SIAM Conference on Mathematics of Data Science 2020 (MDS20, postponed to May 2021) joint with Xiaosheng Zhuang (CityU HK) .