E-mail: congfu@tamu.edu

Short Biography

I am currently a Machine Learning Engineer at Uber. I received my PhD in Computer Science from Texas A&M University. My research focused on leveraging AI to accelerate scientific discovery by developing advanced physical law embedded methods in geometric deep learning, generative modeling, and language models, with applications across biology, materials science, drug discovery, quantum systems, and physical simulations. My advisor is Shuiwang Ji, who leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory. Before TAMU, I received my MS degree in Mechanical Engineering from the University of Michigan.

Research Interests

• Machine Learning

• Graph & Geometric Deep Learning

• AI for Science

• Generative Modeling

News

• 09/2025 Our paper Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations has been accepted to NeurIPS 2025.

• 06/2025 Our paper Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems has been accepted to Foundations and Trends in Machine Learning.

• 01/2025 Our paper Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models has been accepted to ICLR 2025.

• 01/2024 Our paper SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations has been accepted to ICLR 2024.

• 01/2024 Our paper Complete and Efficient Graph Transformers for Crystal Material Property Prediction has been accepted to ICLR 2024.

• 12/2023 Our paper Lattice Convolutional Networks for Learning Ground States of Quantum Many-body Systems has been accepted to SDM 2023.

• 11/2023 Our paper A Latent Diffusion Model for Protein Structure Generation has been accepted to LoG 2023.

• 11/2023 Our paper Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction has been accepted to LoG 2023.

• 11/2023 Check out our AI4Science survey paper Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems! [Github] [Website]

• 04/2023 Our paper Group Equivariant Fourier Neural Operators for Partial Differential Equations has been accepted to ICML 2023.

• 09/2021 Our paper DIG: A Turnkey Library for Diving into Graph Deep Learning Research has been accepted to JMLR.

• 06/17/2021 Our team DIVE@TAMU is an awardee of KDD Cup 2021 on OGB-LSC! [report] [code] [TAMU News]

• 03/2021 Our libray DIG: Dive into Graphs is released! DIG is a research-oriented library that includes unified and extensible implementations of algorithms for graph generation, self-supervised learning on graphs, explainability of graph neural networks, and deep learning on 3D graphs.

Education

• Ph.D., Computer Science, Texas A&M University, Spring 2021 - Summer 2025

• M.S., Mechanical Engineering, University of Michigan -- Ann Arbor, Fall 2016 - Spring 2018

• B.S., Mechanical Engineering, Harbin Institute of Technology, September 2012 - July 2016

Experience

• Applied Geometric DL Scientist Intern, NVIDIA, Santa Clara, CA, September 2024 - December 2024
  Work on protein-protein interaction prediction with GenAI

• PhD Software Engineer Intern, Uber, Sunnyvale, CA, June 2024 - August 2024
  Work on improving ETA prediction accuracy

• Research Intern, Fujitsu Research of America, Sunnyvale, CA, July 2022 - December 2022
  Work on developing SE(3) latent diffusion model for protein backbone generation

• Robotics Engineer, DMAI, Los Angeles, CA, June 2018 - March 2020
  Work as a full-stack robotics engineer to develop mobile and bipedal robot prototypes, including mechanical design, embedded system, control, and navigation.

Preprints

• Cong Fu*, Yuchao Lin*, Zachary Krueger, Wendi Yu, Xiaoning Qian, Byung-Jun Yoon, Raymundo Arróyave, Xiaofeng Qian, Toshiyuki Maeda, Maho Nakata, Shuiwang Ji
  A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials
  [Paper] [Code]

• Cong Fu*, Yuchao Lin*, Zachary Krueger, Haiyang Yu, Maho Nakata, Jianwen Xie, Emine Kucukbenli, Xiaofeng Qian, Shuiwang Ji
  Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
  [Paper] [Code]

Publications

• Yuchao Lin*, Cong Fu*, Zachary Krueger, Haiyang Yu, Maho Nakata, Jianwen Xie, Emine Kucukbenli, Xiaofeng Qian, Shuiwang Ji
  Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2025
  [Paper] [Code]

• Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, Meng Liu, Yuchao Lin, ..., Shuiwang Ji (63 authors)
  Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
  Foundations and Trends in Machine Learning
  [Paper] [Code] [Website]

• Cong Fu*, Xiner Li*, Blake Olson, Heng Ji, Shuiwang Ji
  Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models
  International Conference on Learning Representations (ICLR), 2025
  [Paper] [Code]

• Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
  Complete and Efficient Graph Transformers for Crystal Material Property Prediction
  International Conference on Learning Representations (ICLR), 2024
  [Paper] [Code]

• Xuan Zhang, Jacob Helwig, Yuchao Lin, Yaochen Xie, Cong Fu, Stephan Wojtowytsch, Shuiwang Ji
  SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations
  International Conference on Learning Representations (ICLR), 2024
  [Paper] [Code]

• 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]

• Cong Fu, Jacob Helwig, Shuiwang Ji
  Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction
  Learning on Graphs Conference (LoG), 2023
  [Paper] [Code]

• Cong Fu*, Xuan Zhang*, Huixin Zhang, Hongyi Ling, Shenglong Xu, Shuiwang Ji
  Lattice Convolutional Networks for Learning Ground States of Quantum Many-body Systems
  SIAM International Conference on Data Mining (SDM), 2023
  [Paper] [Code]

• Jacob Helwig*, Xuan Zhang*, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji
  Group Equivariant Fourier Neural Operators for Partial Differential Equations
  International Conference on Machine Learning (ICML), 2023
  [Paper] [Code]

• Meng Liu*, Cong Fu*, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji
  Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
  AI for Science Workshop at NeurIPS, 2021
  Runner-up award of KDD Cup 2021 on OGB-LSC!
  [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, Shuiwang Ji
  DIG: A Turnkey Library for Diving into Graph Deep Learning Research.
  Journal of Machine Learning Research (JMLR), 2021
  [Paper] [Code]

Services

Conference Program Committee Member & Reviewer

• International Conference on Machine Learning (ICML), 2022, 2023, 2024, 2025

• Annual Conference on Neural Information Processing Systems (NeurIPS), 2022, 2023, 2024, 2025

• NeurIPS Track Datasets and Benchmarks, 2023, 2024

• International Conference on Learning Representations (ICLR), 2023, 2024, 2025

• Conference on Information and Knowledge Management (CIKM), 2023, 2024

• Structured Probabilistic Inference & Generative Modeling Workshop @ ICML, 2023

• New Frontiers of AI for Drug Discovery and Development Workshop @ NeurIPS, 2023

• Generative AI and Biology Workshop @ NeurIPS, 2023

• AI4Science Workshop @ NeurIPS, 2022, 2023

Journal Reviewer

• ACM Transactions on Knowledge Discovery from Data (TKDD), 2023

Awards

• Travel Grant, CSE@TAMU, 2023, 2025

• Travel Award, AI4Science Workshop @ NeurIPS, 2021

• Runner-up Award, KDD Cup on Open Graph Benchmark Large-Scale Challenge (OGB-LSC), 2021

• National Scholarship, China, 2014

Teaching Assistant

• CSCE 421 Machine Learning, Texas A&M University, 2023, 2025