Hi, I'm Fang WU!

Welcome to my personal web page! I am a Ph.D. student at Stanford Computer Science, advised by Yejin Choi.I have great fortune to work with Jure Leskovec, James Zou and Brian Hie during the first-year rotation. I was also a member of the reading group organized by Brian Trippe. Previously, I was a research engineer at Tsinghua University advised by Jinbo Xu. I obtained my Master's degree at Columbia University, advised by Dragomir Radev. It is a profound loss for me to lose Prof. Radev on March 29, 2023 (in memoriam). My research focuses on deep learning algorithms for scientific problems — in particular, LLMs, deep generative models, and geometric deep learning.

           
Email: fangwu97 [at] stanford [dot] edu
Address: Stanford, CA, USA
Last update time: 2024.06

News and Highlights

   [2025/05] One papers is accepted by KDD 2025.
   [2025/05] Two papers are accepted by ACL 2025.
   [2025/04] One collaborated paper is accepted by ICML 2025. Congrats to Arthor Deng!
   [2025/02] One paper on mutant effect prediction is accepted by TMLR.
   [2024/12] One paper on dynamic surface modeling is accepted by AAAI 2025.
   [2024/11] One collaborated paper on protein-ligand docking is accepted by Nature Communications.
   [2024/09] One paper on semi-supervised learning is accepted by NeurIPS 2024.
   [2024/03] One paper on GNN bottleneck is accepted by IEEE TKDE.
   [2024/03] One paper is accepted by ICML 2024 and another paper is accepted by IJCAI 2024.
   [2023/10] One paper is accepted by Nature Communications.
   [2023/09] One paper is accepted by NeurIPS 2023.
   [2023/06] One paper is accepted by Communications Biology.
   [2023/04] One paper and another co-authored paper are accepted by ICML 2023.
   [2023/02] One work on molecular domain adaptation is accepted by Cell Patterns.
   [2023/01] A talk on M2D2 invited by MILA and Valence.
   [2022/12] Two papers are accepted by AAAI 2023.
   [2022/10] One work on 3D pretraining is accepted by Advanced Science.

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Research Summary

* represents equal contribution and co-first authorship. † denotes the corresponding author(s).

Large Language Models (LLMs)

   Large Language Models are Good Relational Learners.
   Fang Wu, Vijay Prakash Dwivedi, Jure Leskovec^†
   ACL 2025
   [Paper]    [Code]

   LocAgent: Graph-Guided LLM Agents for Code Localization.
   Zhaoling Chen*, Xiangru Tang*, Gangda Deng*, Fang Wu, Jialong Wu, Zhiwei Jiang, Viktor Prasanna, Arman Cohan, Xingyao Wang^†
   ACL 2025
   [Paper]    [Code]

   When to Trust Context: Self-Reflective Debates for Context Reliability
   Zeqi Zhou*, Fang Wu*^†, Shayan Talaei*, Haokai Zhao, Cheng Meixin, Tinson Xu, Amin Saberi, Yejin Choi
   Under Review
   [Paper]    [Code]

   Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation
   Zerui Xu, Fang Wu, Tianfan Fu, Yue Zhao^†
   Under Review
   [Paper]    [Code]

LLMs for Science

   Integration of Pre-trained Protein Language Mdels into Geometric Deep Learning Networks.
   Fang Wu, Liong Wu, Dragomir Radev, Jinbo Xu, Stan Z. Li^†
   Communications Biology (2023)
   [Paper]    [Code]

Graph Neural Networks (GNNs)

   Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
   Fang Wu, Siyuan Li, Dragomir Radev, Stan Z. Li^†
   ICML 2023
   [Paper]    [Code]

   Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions
   Fang Wu*, Siyuan Li*, Dragomir Radev, Stan Z. Li^†
   IEEE TKDE (2024)
   [Paper]    [Code]

   InsertGNN: A Hierarchical Graph Neural Network for the TOEFL Sentence Insertion Problem
   Fang Wu^†, Stan Z. Li
   EMNLP 2024 Findings
   [Paper]    [Data]

Deep Generative Models for Drug Design

   D-Flow: Multi-modality Flow Matching for D-peptide Design.
   Fang Wu*, Tinson Xu*, Shuting Jin*, Xiangru Tang, Zerui Xu, James Zou, Brian Hie^†
   Under review
   [Paper]    [Code]

   SurfDesign: Effective Protein Design on Molecular Surfaces.
   Fang Wu, Shuting Jin, Jianmin Wang, Zerui Xu, xiangxiang Zeng, Jinbo Xu^†
   Under review
   [Paper]    [Code]

   BC-Design: A Biochemistry-Aware Framework for High-Precision Inverse Protein Folding.
   Xiangru Tang*, Xinwu Ye*, Fang Wu*, Yanjun Shao, Yin Fang, Siming Chen, Dong Xu, Mark Gerstein^†
   Under review
   [Paper]    [Code]

   Surface-based Peptide Design with Multi-modal Flow Matching.
   Fang Wu*, Shuting Jin, Zhengyuan Zhou, Xiangxiang Zeng, Jure Leskovec, Jinbo Xu^†
   KDD 2025
   [Paper]

   A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation.
   Xiangru Tang*, Howard Dai*, Elizabeth Knight*, Fang Wu,, Yunyang Li, Tianxiao Li, Mark Gerstein^†
   Briefings in Bioinformatics (2024)
   [Paper]    [Github Repo.]

   A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design
   Fang Wu, Stan Z. Li^†
   NeurIPS 2023
   [Paper]

3D Geometric Deep Learning

   PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking.
   Yize Jiang*, Xinze Li*, Yuanyuan Zhang*, Jin Han*, Youjun Xu*, Ayush Pandit, Zaixi Zhang, Mengdi Wang, Mengyang Wang, Chong Liu, Guang Yang, Yejin Choi, Wu‑Jun Li^†, Tianfan Fu^†, Fang Wu^†, Junhong Liu^†
   Under Review
   [Paper]    [Code]    [Webpage]

   StaB-ddG: Predicting mutational effects on protein binding from folding energy.
   Arthur Deng, Karsten D. Householder, Fang Wu, Sebastian Thrun, K. Christopher Garcia, Brian L. Trippe^†
   ICML 2025
   [Paper]    [Code]

   Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling.
   Fang Wu, Stan Z. Li^†
   TMLR (2025)
   [Paper]    [Code]

   Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction.
   Houtim Lai^†, Longyue Wang^†, Ruiyuan Qian, Juhong Huang, Peng Zhou, Geyan Ye, Fandi Wu, Fang Wu, Xiangxiang Zeng, Wei Liu
   Nature Communications (2024)
   [Paper]    [Code]

   Surface-VQMAE: Vector-quantized Masked Auto-encoders on Molecular Surfaces.
   Fang Wu, Stan Z. Li^†
   ICML 2024
   [Paper]    [Code]

   Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs.
   Fang Wu, Dragomir Radev, Stan Z. Li^†
   AAAI 2023
   [Paper]    [Code]

   Direct Prediction of Gas Adsorption via Spatial Atom Interaction Learning.
   Jiyu Cui*, Fang Wu*, Wen Zhang*, Lifeng Yang*, Jianbo Hu, Yin Fang, Peng Ye, Qiang Zhang, Xian Suo, Yiming Mo, Xili Cui, Huajun Chen^†, Huabin Xing^†
   Nature Communications (2023)
   [Paper]    [Code]

Molecular Property Prediction

   A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation.
   Fang Wu^†
   IJCAI 2024
   [Paper]    [Code]

   A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation.
   Fang Wu*^†, Shuting Jin*, Siyuan Li, Stan Z. Li
   NeurIPS 2024
   [Paper]    [Code]

   Metric Learning-enhanced Optimal Transport for Biochemical Regression Domain Adaptation
   Fang Wu*, Nicolas Courty*, Shuting Jin*, Stan Z. Li^†
   Patterns (2023)
   [Paper]    [Code]

Molecular Dynamics (MD) Simulations

   Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling.
   Fang Wu, Bozhen Hu, Stan Z. Li^†
   AAAI 2025
   [Paper]

   DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations
   Fang Wu, Stan Z. Li^†
   AAAI 2023 (Oral)
   [Paper]

   Pretraining of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
   Fang Wu*, Shuting Jin*, Yinghui Jiang*, Xurui Jin, Bowen Tang, Zhangming Niu, Qiang Zhang, Xiangxiang Zeng, Stan Z. Li^†
   Advanced Science (2022)
   [Paper]    [Code]

Computer Vision

   SemiReward: A General Reward Model for Semi-supervised Learning
   Siyuan Li*, Weiyang Jin*, Zedong Wang, Fang Wu,, Zicheng Liu, Cheng Tan, Stan Z. Li^†
   ICLR 2024
   [Paper]    [Code]

   Architecture-Agnostic Masked Image Modeling: From ViT back to CNN
   Siyuan Li*, Di Wu*, Fang Wu, Zelin Zang, Kai Wang, Lei Shang, Baigui Sun, Hao Li, Stan Z. Li^†
   ICML 2023
   [Paper]    [Code]

Education

   Stanford University, 2024-now
   • Ph.D. in Computer Science

   Columbia University, 2019-2021
   • Master of Science
   • GPA: 3.51/4.0

   Central University of Economics and Finance, 2015-2019
   • Bachelor of Science
   • GPA: 3.85/4.0

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Industry Experience

   Research Scientist (2023.06-2024.06)
   • BioMap
   • Led by Le Song

   Research Intern (2022.01-2022.07)
   • MindRank
   • Led by Zhangming Niu

Research Experience

Before joining Stanford University, I feel fortunate to be a research assistant/engineer advised by Jinbo Xu at Tsinghua University and Stan Z. Li at Westlake University, and recieved guidance as a visiting student from Huajun Chen, Xiang Bai and Danny Lan.

   Research Student (2024.09-2024.12)
   • Arc Institute
   • Advised by Brian Hie

   Research Engineer (2022.08-2023.05)
   • Tsinghua University
   • Advised by Jinbo Xu

   Research Assistant (2021.11-2022.07)
   • Westlake University
   • Advised by Stan Z. Li


   Visiting Student (2021.03-2021.10)
   • Zhejiang University
   • Hosted by Huajun Chen

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Professional Services

Reviewer: ICLR 2024-2025 , NeurIPS 2023-2025, ICML 2025 , CVPR 2025, ICCV 2025, KDD 2025, AISTATS 2025, IJCAI 2025, ML4H 2023-2024, TMLR, IEEE TNNLS

Teaching: CS224N (2024 Winter)

Acknowledgement

My study cannot be possible without the support from my awesome friends, mentors, and collaborators! Check out some of them:

Prof. Jure Leskovec, Prof. James Zou, Prof. Brian Hie, Prof. Brian Trippe, Dr. Auther Deng at Stanford University.
Prof. Dragomir Radev, Dr. Xiangru Tang at Yale University. R.I.P. to Dr. Dragomir.
Prof. Stan Z. Li, Prof. Danny Lan, Dr. Siyuan Li, Dr. Lirong Wu, Dr. Haitao Lin, Dr. Yufei Huang at Westlake University.
Prof. Jinbo Xu, Prof. Xianyun Zhan, Dr. Shikun Feng at Tsinghua University, and Prof. Wenbing Huang at Renmin University.
Prof. Huajun Chen, Prof. Qiang Zhang, Prof. Huabing Xing, Dr. Jiyu Cui at Zhejiang University.
Prof. Xiangxiang Zeng at Hunan University and Prof. Shuting Jin at Wuhan Technology University.
Prof. Nicolas Courty at University Bretagne Sud.
Prof. Buyong Ma, Prof. Shuangjia Zheng , Prof. Junchi Yan at Shanghai Jiaotong University
Prof. Xiang Bai at Huazhong University of Science and Technology.

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Aside from university collaborations, I also collaborated with many industrial AIDD companies, including MindrankAI, MoleculeMind, and Biomap

Dr. Zhangming Niu, Dr. Xurui Jin, and Dr. Yinghui Jiang at MindrankAI.
Dr. Xiaoyang Jing, Dr. Tenglong Wang, Dr. Wuwei Tan at MoleculeMind.
Prof. Le Song at BioMap.

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