E-mail: limei@tamu.edu | limeiwang.work@gmail.com
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Short Biography

I am a Research Scientist at Meta. Prior to that, I received my Ph.D. degree in Computer Engineering from Texas A&M University in 2024, under the supervision of Prof. Shuiwang Ji. My research interests include machine learning, deep learning on graphs, sequence modeling, recommendation, and AI for Science.

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

• AI for Science, Graph/Geometric ML for Molecules (molecules, proteins, materials, drug discovery, …) [Related papers: SphereNet, ComENet, ProNet, LEFTNet, LatentDiff, Geo2Seq, AIRS survey ]

• Scalable Methods for Large-Scale Graphs, Graph Transformer, Graph Foundation Models (social network, citation network, …) [Related papers: GraphFM, S3, GQT ]

• Generative Models [Related papers: LatentDiff ]

• Large Language Models [Related papers: Geo2Seq ]

News

  [05/2025] Geo2Seq is accepted to ICML 2025.

  [01/2025] GQT is accepted to ICLR 2025.

  [05/2024] Graduate from Texas A&M.

  [02/2024] Join Meta as a Research Scientist.

  [11/2023] LatentDiff is accepted to LoG 2023.

  [09/2023] LEFTNet is accepted to NeurIPS 2023.

  [08/2023] Selected as EECS Rising Stars 2023. [Past year’s workshops]

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

  [05/2023] Join Meta as a Research Intern.

  [01/2023] ProNet is accepted to ICLR 2023.

  [09/2022] ComENet is accepted to NeurIPS 2022.

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

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

  [06/2022] GraphFM is accepted to ICML 2022.

  [02/2022] AdvProp is accepted to Bioinformatics.

  [01/2022] SphereNet is accepted to ICLR 2022.

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

  [09/2021] DIG is 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.

Xiner Li*, Limei Wang*, Youzhi Luo, Carl Edwards, Shurui Gui, Yuchao Lin, Heng Ji, Shuiwang Ji Learning to Generate 3D Molecules via Language Models with Geometry-Aware Tokenization International Conference on Machine Learning (ICML), 2025 [paper]

Limei Wang*, Kaveh Hassani*, Si Zhang, Dongqi Fu, Baichuan Yuan, Weilin Cong, Zhigang Hua, Hao Wu, Ning Yao, Bo Long Learning Graph Quantized Tokenizers International Conference on Learning Representations (ICLR), 2025 [paper]

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], [code]

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.

Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong How to Make LLMs Strong Node Classifiers?

Limei Wang, Si Zhang, Hanqing Zeng, Hao Wu, Zhigang Hua, Kaveh Hassani, Andrey Malevich, Bo Long, Shuiwang Ji Staleness-based Subgraph Sampling for Large-Scale GNNs Training [paper]

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

• 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, 2024

• 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, 2025