Meng Liu [刘猛]

Bio

Howdy! I am currently a Ph.D. candidate in the Department of Computer Science & Engineering, Texas A&M University, advised by Prof. Shuiwang Ji. I obtained my bachelor’s degree from the Department of Electronic Engineering, Tsinghua University in 2019, advised by Prof. Liangrui Peng. My research interests are deep learning and machine learning. I am currently working on the following directions and their intersections.

Geometric deep learning: Develop principle algorithms to learn from data that has an underlying geometric structure, such as graphs (social networks, co-purchase networks, etc.) and 3D geometries (molecules, proteins, etc.).
AI for science: Design data-driven approaches for scientific discovery, such as predicting molecular properties and understanding protein-ligand binding.
Generative modeling: Capture data distributions and generate new samples for complex data, especially for discrete and graph data.

News

9/2023

Our paper Video Timeline Modeling for News Story Understanding has been accepted as a spotlight to NeurIPS 2023, Track on Datasets and Benchmarks.

9/2023

Our paper QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules has been accepted to NeurIPS 2023, Track on Datasets and Benchmarks.

9/2023

Our paper Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization has been accepted to NeurIPS 2023.

4/2023

Our paper Graph Mixup with Soft Alignments has been accepted to ICML 2023.

3/2023

I am so honored to receive the TAMU CSE Graduate Research Excellence Award.

Publications [Google Scholar]

* indicates equal contribution.

Preprint

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

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

Paper Code

DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding

Haitao Lin*, Yufei Huang*, Meng Liu, Xuanjing Li, Shuiwang Ji, and Stan Z. Li

Paper

Empowering GNNs via Edge-Aware Weisfeiler-Lehman Algorithm

Meng Liu, Haiyang Yu, and Shuiwang Ji

Paper Paper collection

2023

Video Timeline Modeling for News Story Understanding

Meng Liu, Mingda Zhang, Jialu Liu, Hanjun Dai, Ming-Hsuan Yang, Shuiwang Ji, Zheyun Feng, and Boqing Gong

Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks, 2023

Spotlight

Paper Code

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

Haiyang Yu*, Meng Liu*, Youzhi Luo, Alex Strasser, Xiaofeng Qian, Xiaoning Qian, and Shuiwang Ji

Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks, 2023

Paper Code

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, Shuiwang Ji

Neural Information Processing Systems (NeurIPS), 2023

Paper Code

Graph Mixup with Soft Alignments

Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji, and Na Zou

International Conference on Machine Learning (ICML), 2023

Paper Code

Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models

Meng Liu, Haoran Liu, and Shuiwang Ji

International Conference on Learning Representations (ICLR), 2023

Paper Code

2022

Generating 3D Molecules for Target Protein Binding

Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji

International Conference on Machine Learning (ICML), 2022

Oral (118/5630=2.1% acceptance rate)

Paper Code

Video

GraphFM: Improving Large-Scale GNN Training via Feature Momentum

Haiyang Yu*, Limei Wang*, Bokun Wang*, Meng Liu, Tianbao Yang, and Shuiwang Ji

International Conference on Machine Learning (ICML), 2022

Paper Code

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery

Zhengyang Wang*, Meng Liu*, Youzhi Luo*, Zhao Xu*, Yaochen Xie*, Limei Wang*, Lei Cai*, Qi Qi, Zhuoning Yuan, Tianbao Yang, and Shuiwang Ji

Bioinformatics, 2022

Rank #1 on AI Cures open challenge for COVID-19

Paper Code

Supplement

Spherical Message Passing for 3D Molecular Graphs

Yi Liu*, Limei Wang*, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji

International Conference on Learning Representations (ICLR), 2022

Paper Code

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

Meng Liu and Shuiwang Ji

SIAM International Conference on Data Mining (SDM), 2022

Paper Code Supplement

2021

Non-Local Graph Neural Networks

Meng Liu*, Zhengyang Wang*, and Shuiwang Ji

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021

Paper Code Paper collection

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

Meng Liu*, Cong Fu*, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, and Shuiwang Ji

AI4Science Workshop at NeurIPS, 2021

Awardee of KDD Cup 2021 on OGB-LSC

Paper Code

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

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

Journal of Machine Learning Research (JMLR), 2021

Join the DIG slack community!

Paper Code

Documentation

GraphEBM: Molecular Graph Generation with Energy-Based Models

Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji

EBM Workshop at ICLR, 2021

Paper Code

2020

Towards Deeper Graph Neural Networks

Meng Liu, Hongyang Gao, and Shuiwang Ji

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020

Paper Code

Paper collection

Services

Session Chair

International Conference on Machine Learning (ICML) 2022

Program Committee Member & Reviewer [Selected]

Annual Conference on Neural Information Processing Systems (NeurIPS) 2021, 2022 (Award), 2023

NeurIPS Track Datasets and Benchmarks 2022, 2023

International Conference on Machine Learning (ICML) 2021, 2022 (Award), 2023

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021, 2022

International Conference on Learning Representation (ICLR) 2020, 2021, 2022, 2023, 2024

International Conference on Computer Vision (ICCV) 2023

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022, 2023

European Conference on Computer Vision (ECCV) 2022

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023

SIAM International Conference on Data Mining (SDM) 2022

Journal of Machine Learning Research (JMLR)

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Transactions on Machine Learning Research (TMLR)

Neurocomputing

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

BMC Bioinformatics

Vitæ

Here is my CV[PDF].

Texas A&M University Aug 2019 - now

Ph.D. Student
Advisor: Prof. Shuiwang Ji
Computer Science & Engineering
Google Fall 2022

Research Intern
Mentor: Zheyun Feng, Boqing Gong
Meta AI Summer 2022

Research Intern
Mentor: Xing Wang
Fujitsu Research of America Summer 2021

Research Intern
Mentor: Kanji Uchino
Tsinghua University Aug 2015 - Jul 2019

B.E. Student
Advisor: Prof. Liangrui Peng
Electronic Engineering
Owlii Summer 2018

R&D Intern
Mentor: Yi Xu