wanyu.jpg

Department of Computing

The Hong Kong Polytechnic University

PQ730, Mong Man Wai Building

Hung Hom, Kowloon, Hong Kong

Wanyu is an Assistant Professor in the Department of Computing at The Hong Kong Polytechnic University. Wanyu is serving as an Associate Editor for  IEEE Transactions On Neural Networks and Learning Systems (TNNLS).

Wanyu’s primary research interest is in 1) AI for science. We focus on developing machine learning (ML) techniques (GNNs, diffusion, etc.) to accelerate scientific simulations and design (applications in materials, structure safety); 2) creating AI with safe and ethical objectives. Specifically, we focus on interpretability, robustness, and privacy-preservation of machine learning models, such as large language models.


Research Team

  • Jian Chen (Post-Doctoral Fellow, Previously Ph.D. at HUST)
  • Zhenzhong Wang (Ph.D. Candidate, Previously Master Student at Xiamen University)
  • Yee Chung Cheung (Doctor of FinTech Candidate)
  • Mingxuan Ouyang (Ph.D. Candidate, Previously Master Student at HK PolyU)
  • Haowei Hua (Ph.D. Candidate, Previously Master Student at University of Electronic Science and Technology of China (UESTC))
  • Jiangwen Dong (Ph.D. Candidate, Previously Undergraduate Student at Zhejiang University)
  • Zhuoran Li (Research Assistant, Previously Undergraduate at University of Washington-Seattle)
  • Zehui Lin (Research Assistant, Previously Undergraduate Student at University of Sydney)
  • Xu Sun (Research Assistant, Undergraduate Student at HK PolyU)

  • We are looking for motivated Post-Doctoral Fellows and Ph.D. Students who are interested and experienced in AI for Science and trustworthy AI. Send me your CV (GPA, publications, etc.) and your transcript via email if you are interested in working with me at PolyU. Candidates who have strong mathematics backgrounds and programming skills are preferred.

news

Jun 11, 2024 My first P.h.d. student, Zhenzhong Wang, will join Xiamen University as an Assistant Professor in this coming Fall. :sparkles:
Jun 11, 2024 Our paper, Graph Privacy Funnel: A Variational Approach for Privacy-Preserving Representation Learning on Graphs, was accepted as a Regular Paper in the Transactions on Dependable and Secure Computing. :smile:
Jun 2, 2024 Serving as a Track Chair for ICPADS 2024
Jun 2, 2024 Serving as a Area Chair for ICML 2024 workshop AI4Science.
May 2, 2024 Our paper, Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration , was accepted by ICML 2024. :smile:
Dec 10, 2023 Our two papers, Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks and Self-Prompt Dehazing Transformers with Depth-Consistency, were accepted by AAAI 2024. :smile:
Nov 24, 2023 Our paper, Predicting Dynamic Responses of Continuous Deformable Bodies: A Graph-Based Learning Approach, was accepted by Computer Methods in Applied Mechanics and Engineering. :sparkles:
Jul 27, 2023 Invited talk titled “Towards Generative Causal Explanations for Graph Neural Networks” at the International Scholars Forum, hosted by Zhejiang University.