Limei Wang, final-year Ph.D. student Department of Computer Science & Engineering Texas A&M University 364 L.F. Peterson Building College Station, TX 77843-3112 E-mail: limei@tamu.edu Google Scholar | Github | Twitter | LinkedIn CV[PDF]

Short Biography

I am a Ph.D. candidate in Department of Computer Science & Engineering, Texas A&M University. My advisor is Dr. Shuiwang Ji, who leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory. My research interests include machine learning, deep learning, and graph analysis. Currently, I am working on graph neural networks and their applications to molecular property prediction and drug discovery. I have also worked on large-scale graphs with massive numbers of nodes and edges.

During Ph.D., I interned at Meta (with Dr. Si Zhang) as a research intern in summer 2023.

Before TAMU, I obtained my bachelor’s degree in Automation at University of Science and Technology of China (USTC) in June 2019.

Research Interests

• Machine Learning, Deep Learning

• Graph/Geometric ML for Molecules (molecules, proteins, materials, drug discovery, …) [Related papers: SphereNet, ComENet, ProNet, LEFTNet]

• Scalable Methods for Large-Scale Graphs (social network, citation network, …) [Related papers: GraphFM]

• Graph Generative Models [Related papers: LatentDiff]

News

  [11/2023] One paper is accepted to LoG 2023.

  [09/2023] One paper is accepted to NeurIPS 2023.

  [08/2023] Selected to participate in the EECS Rising Stars 2023 Workshop! [Past year’s workshops]

  [07/2023] Our AI4Science survey paper is released! [AIRS library], [Website]

  [01/2023] One paper is accepted to ICLR 2023.

  [09/2022] One paper is accepted to NeurIPS 2022.

  [09/2022] One paper is accepted to NeurIPS 2022 Datasets and Benchmarks.

  [08/2022] Our tutorial Frontiers of Graph Neural Networks with DIG is accepted to KDD. [Hands-on Tutorial]

  [06/2022] One paper is accepted to ICML 2022.

  [02/2022] One paper is accepted to Bioinformatics.

  [01/2022] One paper is accepted to ICLR 2022.

  [12/2021] DIVE@TAMU is ranked #3 on the Open Catalyst Challenge 2021.

  [09/2021] One paper accepted to JMLR. [DIG library]

  [06/2021] DIVE@TAMU is an awardee of KDD Cup 2021 on OGB-LSC. [Technical report], [Code]

Publications

* indicates equal contribution. † indicates equal senior contribution.

Cong Fu*, Keqiang Yan*, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji A Latent Diffusion Model for Protein Structure Generation Learning on Graphs Conference (LoG), 2023 [paper]

Weitao Du*, Yuanqi Du*, Limei Wang*, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, Zhi-Ming Ma A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 [paper], [code]

Limei Wang*, Haoran Liu*, Yi Liu*†, Jerry Kurtin, and Shuiwang Ji† Learning Hierarchical Protein Representations via Complete 3D Graph Networks International Conference on Learning Representations (ICLR), 2023 [paper], [code]

Limei Wang*, Yi Liu*, Yuchao Lin, Haoran Liu, and Shuiwang Ji ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS), 2022 [paper], [code], [poster]

Shurui Gui*, Xiner Li*, Limei Wang, and Shuiwang Ji GOOD: A Graph Out-of-Distribution Benchmark Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (NeurIPS Datasets and Benchmarks), 2022 [paper], [code], [document]

Haiyang Yu*, Limei Wang*, Bokun Wang*, Meng Liu, Tianbao Yang, and Shuiwang Ji GraphFM: Improving Large-Scale GNN Training via Feature Momentum International Conference on Machine Learning (ICML), 2022 [paper], [code]

Zhengyang Wang*, Meng Liu*, Youzhi Luo*, Zhao Xu*, Yaochen Xie*, Limei Wang*, Lei Cai*, Qi Qi, Zhuoning Yuan, Tianbao Yang, and Shuiwang Ji Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery Bioinformatics, 2022 [paper], [code] [Rank #1 on AI Cures challenge for COVID-19]

Yi Liu*, Limei Wang*, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji Spherical Message Passing for 3D Molecular Graphs International Conference on Learning Representations (ICLR), 2022 [paper], [code]

Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, and Shuiwang Ji DIG: A Turnkey Library for Diving into Graph Deep Learning Research Journal of Machine Learning Research (JMLR), 2021 [paper], [code], [document]

Meng Liu*, Cong Fu*, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, and Shuiwang Ji Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks AI for Schence Workshop at NeurIPS, 2021 [paper], [code]

Yingying Wang*, Lei Cai*, Wei Chen, Difei Wang, Shi Xu, Limei Wang, Martin A Kononov, Shuiwang Ji, Ming Xian Development of Xanthene-Based Fluorescent Dyes: Machine Learning-Assisted Prediction vs. TD-DFT Prediction and Experimental Validation Chemistry-Methods, 2021 [paper]

Preprints

* indicates equal contribution.

Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, YuQing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [paper], [code], [website]

Education

• Ph.D., Computer Engineering, Texas A&M University, September 2019 - May 2024 (Expected)

• B.E., Automation, University of Science and Technology of China, September 2015 - July 2019

Services

Conference Reviewer

• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022

• International Conference on Machine Learning (ICML), 2022, 2023

• Conference on Neural Information Processing Systems (NeurIPS), 2022, 2023

• NeurIPS Datasets and Benchmarks Track, 2022, 2023

• AI4Science Workshop @ NeurIPS, 2022, 2023

• Learning on Graphs Conference (LoG), 2022, 2023

• International Conference on Learning Representations (ICLR), 2023, 2024