Haoyang Li   李昊阳


Postdoctoral Associate, Weill Cornell Medicine
Cornell University

Image 1 Image 2

Email: lihy218 [at] gmail [dot] com


I'm a postdoctoral associate at Weill Cornell Medicine of Cornell University, working with Prof. Fei Wang. Prior to that, I got my Ph.D. in the Department of Computer Science and Technology at Tsinghua University in 2023, advised by Prof. Wenwu Zhu, Prof. Peng Cui and Prof. Xin Wang. I got my Bachelor degree in the Department of Computer Science and Technology at Tsinghua University in 2018. I also got my Second Bachelor Degree in School of Economics and Management at Tsinghua University in 2018.

My research interests are mainly in Machine Learning on Graphs, including graph neural network, out-of-distribution (OOD) generalization, causal inference, and AI4Science.


News

  • [Oct 2023] One tutorial regarding graph OOD generalization is accepted by AAAI 2024! See you in Vancouver!
  • [Aug 2023] We have released the perspective paper of large graph model and paper collections!
  • [Aug 2023] One paper regarding multimedia cognition won best paper award at ACM MM 2023 workshop on multimedia content generation and evaluation!
  • [Jun 2023] One paper regarding node-level OOD generalization on graphs is accepted by TOIS 2023!
  • [Apr 2023] One tutorial regarding graph OOD generalization is accepted by IJCAI 2023! See you in Macao!
  • [Apr 2023] One survey paper regarding curriculum learning on graphs is accepted by IJCAI 2023!
  • [Feb 2023] One paper regarding commonsense knowledge graph for recommendation is accepted by ICDE 2023 (TKDE Poster Session Track)!
  • [Jan 2023] One tutorial regarding graph OOD generalization is accepted by WWW 2023! See you in Austin!
  • [Jan 2023] One paper regarding automated graph transformer is accepted by ICLR 2023 (Top-5%)!
  • [Jan 2023] We have released the updated version of graph OOD generalization survey and website! More recent works are included!
  • [Dec 2022] The new version v0.4 of AutoGL, a toolkit and platform towards automatic machine learning on graphs, has been released on Github!

Publications

2024

  1. [24]
    Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu OOD-GNN: Out-of-Distribution Generalized Graph Neural Network (Extended Abstract) ICDE 2024, IEEE International Conference on Data Engineering. Extended Abstract. [paper]
  2. [23]
    Chen Huang, Haoyang Li, Yifan Zhang, Wenqiang Lei, Jiancheng Lv Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem WWW 2024, The Web Conference. [paper] [code]
  3. [22]
    Tianrui Jia, Haoyang Li, Cheng Yang, Tao Tao, Chuan Shi Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization AAAI 2024, Thirty-Eighth AAAI Conference on Artificial Intelligence. [paper]
  4. [21]
    Jie Cai, Xin Wang, Haoyang Li, Ziwei Zhang, Wenwu Zhu Multimodal Graph Neural Architecture Search under Distribution Shifts AAAI 2024, Thirty-Eighth AAAI Conference on Artificial Intelligence. [paper]

2023

  1. [20]
    Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Simin Wu, Wenwu Zhu LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs? Under Review, [paper]
  2. [19]
    Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, Wenwu Zhu Graph Meets LLMs: Towards Large Graph Models NeurIPS 2023 GLFrontiers Workshop, Workshop on New Frontiers in Graph Learning. [paper]
  3. [18]
    Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue, Haoyang Li, Wenwu Zhu Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts NeurIPS 2023, Advances in Neural Information Processing Systems. [paper]
  4. [17]
    Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Wenwu Zhu Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion Under Review, [paper]
  5. [16]
    Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu Out-Of-Distribution Generalization on Graphs: A Survey Under Review, [paper] [website]
  6. [15]
    Wei Feng*, Haoyang Li*, Xin Wang, Xuguang Duan, Zi Qian, Wenwu Zhu Multimedia Cognition and Evaluation in Open Environments MM 2023 McGE Workshop, Workshop on Multimedia Content Generation and Evaluation. Best Paper Award. [paper]
  7. [14]
    Haoyang Li, Ziwei Zhang, Xin Wang, Wenwu Zhu Invariant Node Representation Learning under Distribution Shifts with Multiple Latent Environments TOIS 2023, ACM Transactions on Information Systems. [paper]
  8. [13]
    Haoyang Li, Xin Wang, Wenwu Zhu Curriculum Graph Machine Learning: A Survey IJCAI 2023, International Joint Conference on Artificial Intelligence. [paper]
  9. [12]
    Yuling Wang, Xiao Wang, Xiangzhou Huang, Yanhua Yu, Haoyang Li, Mengdi Zhang, Zirui Guo, Wei Wu Intent-aware Recommendation via Disentangled Graph Contrastive Learning IJCAI 2023, International Joint Conference on Artificial Intelligence. [paper]
  10. [11]
    Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, Wenwu Zhu Intention-aware Sequential Recommendation with Structured Intent Transition ICDE 2023, IEEE International Conference on Data Engineering. Extended Abstract. [paper]
  11. [10]
    Zizhao Zhang, Xin Wang, Chaoyu Guan, Ziwei Zhang, Haoyang Li, Wenwu Zhu AutoGT: Automated Graph Transformer Architecture Search ICLR 2023, International Conference on Learning Representations. Top-5%, Oral. [paper] [code]

2022

  1. [9]
    Haoyang Li, Ziwei Zhang, Xin Wang, Wenwu Zhu Learning Invariant Graph Representations for Out-of-Distribution Generalization NeurIPS 2022, Advances in Neural Information Processing Systems. [paper] [code] [poster]
  2. [8]
    Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Zhou Qin, Wenwu Zhu. Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift NeurIPS 2022, Advances in Neural Information Processing Systems. [paper] [code]
  3. [7]
    Haoyang Li, Ziwei Zhang, Xin Wang, Wenwu Zhu Disentangled Graph Contrastive Learning With Independence Promotion TKDE 2022, IEEE Transactions on Knowledge and Data Engineering. [paper] [code]
  4. [6]
    Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu OOD-GNN: Out-of-Distribution Generalized Graph Neural Network TKDE 2022, IEEE Transactions on Knowledge and Data Engineering. [paper] [code]

2021

  1. [5]
    Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu Disentangled Contrastive Learning on Graphs NeurIPS 2021, Advances in Neural Information Processing Systems. [paper] [code] [slides]
  2. [4]
    Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, Wenwu Zhu Intention-aware Sequential Recommendation with Structured Intent Transition TKDE 2021, IEEE Transactions on Knowledge and Data Engineering. [paper] [code] [slides]
  3. [3]
    Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu AutoGL: A Library for Automated Graph Learning ICLR 2021 GTRL Workshop, Workshop on Geometrical and Topological Representation Learning. [paper] [website]

< 2020

  1. [2]
    Haoyang Li, Peng Cui, Chengxi Zang, Tianyang Zhang, Wenwu Zhu, Yishi Lin Fates of Microscopic Social Ecosystems: Keep Alive or Dead? KDD 2019, SIGKDD Conference on Knowledge Discovery and Data Mining. Oral. [paper]
  2. [1]
    Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu Billion-scale network embedding with iterative random projection ICDM 2018, IEEE International Conference on Data Mining. Oral, acceptance rate 8.9%. [paper] [code] [slides]

Projects

AutoGL: A toolkit and platform towards automatic machine learning on graphs.

Mesh Simplification based on Edge-collapse Algorithm.


Talks and Presentations

Out-Of-Distribution Generalization on Graphs

  • Invited by Tencent AI LAB, Jun. 2022 (Online)

Disentangled Contrastive Learning on Graphs

  • Invited by the Thirty-Fifth NeurIPS Conference, Dec. 2021 (Online)

Intention-aware Sequential Recommendation

  • Invited by Beijing Academy of Artificial Intelligence (BAAI), Mar. 2021 (Beijing)

Fates of Microscopic Social Ecosystems: Keep Alive or Dead?

  • Invited by the Twenty-Fifth ACM SIGKDD Conference, Aug. 2019 (Alaska)

Research Overview of Social Dynamics Modeling

  • Invited by ByteDance AI LAB, Jul. 2019 (Beijing)


Professional Services

Journal reviewer: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Signal and Information Processing over Networks (TSIPN), Journal of Biomedical Informatics (JBI), Expert Systems with Applications (ESWA)

Conference reviewer / Program committee / External reviewer: ICML (2021, 2022, 2023, 2024), NeurIPS (2021, 2022, 2023), KDD (2021, 2022, 2023, 2024), ICLR (2021, 2022, 2023, 2024), AAAI (2022, 2023), IJCAI (2022, 2023, 2024), WWW (2022, 2023), WSDM (2023), LOG (2022, 2023)


Awards and Fellowships

  • Beijing Outstanding Graduates (2023)
  • Tsinghua Outstanding Doctoral Dissertation (2023)
  • Tsinghua ‘84’ Future Innovation Scholarship (2022)
  • Tsinghua Comprehensive Excellence Scholarship (2019, 2021)
  • Tsinghua Academic Excellence Scholarship (2017)

Teaching

  • TA in Big Data Analytics (B) (Fall 2019, 2020, 2021, 2022)
  • TA in Big Data Analytics and Processing (Spring 2019, 2020, 2021, 2022)