Zhangyang Gao (高张阳)
Young Researcher, PJLAB

PJLAB
Agentic Biology and Virtual Cell Research Lead

Location: Shanghai, China
News | Research Interest | Projects | Education | Publications | Services | Awards

Email: gaozhangyang@pjlab.org.cn
[Google Scholar] [GitHub] [ResearchGate] [ORCID]

Welcome to contacting me about research or internship by emails or WeChat (simpler_yet_better).
I lead research on agentic biology and virtual cells, and welcome collaborations in AI4Science.

News

[2026.06] One paper, Toward the construction of a virtual yeast, has been accepted by Nature.
[2026.06] One paper, MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation, has been accepted by ICML 2026 as a regular paper.
[2026.06] One paper on, benchmarking virtual cell models, is under review by Nature Machine Intelligence.
[2026.03] One paper on, virtual cell foundation models, SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction, has been released.
[2026.03] One paper on, single-cell foundation models, has been published by Nature Communications.
[2025.09] Two papers on, protein inverse folding, have been accepted by NeurIPS 2025, including one Spotlight.
[2025.08] I joined PJLAB as a Young Researcher and Agentic Biology and Virtual Cell Research Lead.
[2025.06] One paper on, cryo-EM complex structure determination, has been published by Nature Machine Intelligence.
[2025.2] One paper on, De Novo Mass Spectrometry Peptide Sequencing, has been accepted by ICLR 2025 , congrats to Shaorong Chen.
[2025.2] One paper on, Protein Motif-Scaffolding, has been accepted by ICLR 2025 , congrats to Yufei Huang.
[2025.2] One paper on, Protein Post-Translational Modification Prediction, has been accepted by ICLR 2025 , congrats to Cheng Tan.
[2024.10] One paper on, Protein language, has been accepted by AAAI 2025 , congrats to Cheng Tan.
[2024.10] One paper on, Antibody design, has been accepted by AAAI 2025 (Oral) , congrats to Cheng Tan.
[2024.10] One paper on, Antibody design, has been accepted by AAAI 2025 , congrats to Lirong Wu.
[2024.09] One paper on, Protein sequence design, has been accepted by NeurIPS 2024
[2024.09] One paper on, Protein representation learning, has been accepted by NeurIPS 2024, congrats to Bozhen Hu.
[2024.09] One paper on, Peptide Sequencing in Proteomics, has been accepted by NeurIPS 2024, congrats to Jun Xia.
[2024.05] One paper on, Self-supervised Graph Language Modeling, has been accepted by ICML 2024
[2024.05] One paper on, Molecule Docking, has been accepted by ICML 2024, congrats to Yufei Huang.
[2024.03] One paper on, Protein Sequence Design, has been accepted by ICLR 2024
[2024.03] One paper on, graph distillation, has been accepted by TKDE, congrats to Lirong Wu.
[2024.03] One paper on, Medical Image Representation Learning, has been accepted by CVPR 2024, congrats to Zhe Li.
[2024.03] One paper on, point cloud pretraining, has been accepted by CVPR 2024, congrats to Zhe Li.
[2024.03] One paper on, self-supervised graph learning, has been accepted by ICDE 2024, congrats to Jun Xia.
[2024.02] One survey on, diffusion model, has been accepted by TKDE, congrats to Hanqun Cao.
[2023.12] One paper on, compound-protein interaction, has been accepted by TKDE, congrats to Lirong Wu.
[2023.12] One paper on, Molecule Representation Learning, has been accepted by AAAI 2024, congrats to Yufei Huang.
[2023.12] One paper on, antibody design, has been accepted by AAAI 2024, congrats to Cheng Tan.
[2023.09] One paper on, Protein Sequence Design, has been accepted by NeurIPS 2023
[2023.09] One paper on, video prediction, has been accepted by NeurIPS 2023, congrats to Cheng Tan.
[2023.06] One paper on, molecule pretraining, has been accepted by ECML 2023.
[2023.06] One paper on, molecule generation, has been accepted by ECML 2023, congrats to Cheng Tan.
[2023.03] One paper on, Protein Sequence Design, has been accepted by ICLR 2023 (Spotlight)
[2023.03] One paper on, molecule pretraining, has been accepted by ICLR 2023, congrats to Jun Xia.
[2023.03] One paper on, protein sequence design, has been accepted by ICASSP 2023, congrats to Cheng Tan.
[2023.03] One paper on, video prediction, has been accepted by CVPR 2023, congrats to Cheng Tan.
[2023.01] One paper on, graph learning, has been accepted by TNNLS, congrats to Lirong Wu.
[2022.03] One paper on, Video Prediction, has been accepted by CVPR 2022
[2022.03] One paper on, semi-supervised learning, has been accepted by CVPR 2022, congrats to Cheng Tan.
[2021.12] One survey on, graph self-supervised learning, has been accepted by TKDE, congrats to Lirong Wu.
[2021.12] One paper on, spatio-temporal prediction, has been accepted by AAAI 2021, congrats to Haitao Lin.

Research Interest

Currently, I focus on the following research topics:

Agentic biology and virtual cells
Virtual cell and biomolecular foundation models
Spatio-temporal predictive learning
AI4Science (protein/RNA design, single-cell modeling)

Open-Source Projects

AGI4Sci/SciForge: an actively maintained agent project for scientific research. SciForge is a self-evolving multimodal agent workbench that organizes scientific data, papers, code, interactive UI, execution traces, and human feedback into auditable and reusable research artifacts.
A4Bio Open-Source Ecosystem: a collection of AI4Bio and AI4Science projects that I maintain and update, covering biomolecular design, virtual cells, graph foundation models, cryo-EM, and spatio-temporal modeling.
- Protein, antibody, and RNA design: PiFold, ProteinInvBench, FoldToken_open, FoldGPT_open, FoldMaskGPT, DiffSDS, ADesigner, dyAb, RDesign, R3Design, RFold.
- Cell, omics, and structure modeling: CellVQ, MeToken, E3-CryoFold.
- Molecule, graph, and scientific ML tools: GraphsGPT, PrefixMol, MotifRetro, SimVP, LightningFlow.

Education

2025.08-present Young Researcher at PJLAB.
2020.09-2025.06 Ph.D in CAIRI, Westlake University. Supervisor: Prof. Stan Z. Li
2016.09-2020.06 B.E. in School of Automation, Central South University. Ranks first in the major and college.

Publications

Recent Publications (2025-2026):

MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation framework figure

MindFlow: Mind Supernet Powered Thinking Flows for Research Idea Innovation
Zhangyang Gao et al.
ICML, 2026 (Regular Paper) [OpenReview]

Toward the construction of a virtual yeast framework figure

Toward the construction of a virtual yeast
Zhangyang Gao et al.
Nature, 2026 (accepted)

Illuminating cell states by a comprehensive and interpretable single cell foundation model Jue Wang, Cheng Tan, Zhangyan framework figure

Illuminating cell states by a comprehensive and interpretable single cell foundation model
Jue Wang, Cheng Tan, Zhangyang Gao, Sida Shao, Shiping Liu, Stan Z. Li
Nature Communications, 2026 [DOI]

Benchmarking virtual cell models for in-the-wild perturbation response Xinjie Mao, Songming Zhang, Qirong Wen, Xiangyu W framework figure

Benchmarking virtual cell models for in-the-wild perturbation response
Xinjie Mao, Songming Zhang, Qirong Wen, Xiangyu Wen, Kedu Jin, Hao Wu, Shuizhou Chen, Yuqiang Li, Lei Bai, Qi Liu, Ning Ding, Siqi Sun, Zhangyang Gao
Under review by Nature Machine Intelligence, 2026 [PDF] [arXiv]

AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories Xue Xia, Chengkai Yao, Mingyu Tsoi, Xinjie Mao, framework figure

AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories
Xue Xia, Chengkai Yao, Mingyu Tsoi, Xinjie Mao, Wenxuan Huang, Jiaqi Wei, Hao Wu, Cheng Tan, Lang Yu, Yuejin Yang, Mengdi Liu, Siqi Sun, Zhangyang Gao
arXiv, 2026 [PDF] [arXiv]

HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts Wenxuan Huang, Mingyu T framework figure

HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts
Wenxuan Huang, Mingyu Tsoi, Yanhao Huang, Xinjie Mao, Xue Xia, Hao Wu, Jiaqi Wei, Yuejin Yang, Lang Yu, Cheng Tan, Xiang Zhang, Zhangyang Gao, Siqi Sun
arXiv, 2026 [PDF] [arXiv]

SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction Shuizhou Chen, Lang framework figure

SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction
Shuizhou Chen, Lang Yu, Kedu Jin, Songming Zhang, Hao Wu, Wenxuan Huang, Sheng Xu, Quan Qian, Qin Chen, Lei Bai, Siqi Sun, Zhangyang Gao
arXiv, 2026 [PDF] [arXiv]

SciDataCopilot: An Agentic Data Preparation Framework for AGI-driven Scientific Discovery Jiyong Rao, Yicheng Qiu, Jiahu framework figure

SciDataCopilot: An Agentic Data Preparation Framework for AGI-driven Scientific Discovery
Jiyong Rao, Yicheng Qiu, Jiahui Zhang, Juntao Deng, Shangquan Sun, Fenghua Ling, Hao Chen, Nanqing Dong, Zhangyang Gao, Siqi Sun, Yuqiang Li, Dongzhan Zhou, Guangyu Wang, Lijun Wu, Conghui He, Xuhong Wang, Jing Shao, Xiang Liu, Yu Zhu, Mianxin Liu, Qihao Zheng, Yinghui Zhang, Jiamin Wu, Xiaosong Wang, Shixiang Tang, Wenlong Zhang, Bo Zhang, Wanli Ouyang, Runkai Zhao, Chunfeng Song, Lei Bai, Chi Zhang
arXiv, 2026 [PDF] [arXiv]

USTEP: Spatio-Temporal Predictive Learning Under a Unified View Cheng Tan, Jue Wang, Zhangyang Gao, Siyuan Li, Stan Z. L framework figure

USTEP: Spatio-Temporal Predictive Learning Under a Unified View
Cheng Tan, Jue Wang, Zhangyang Gao, Siyuan Li, Stan Z. Li
TPAMI, 2025 [PDF] [DOI]

SimVPv2: Towards Simple Yet Powerful Spatiotemporal Predictive Learning Cheng Tan, Zhangyang Gao, Siyuan Li, Stan Z. Li framework figure

SimVPv2: Towards Simple Yet Powerful Spatiotemporal Predictive Learning
Cheng Tan, Zhangyang Gao, Siyuan Li, Stan Z. Li
TMM, 2025 [PDF] [DOI]

End-to-end cryo-EM complex structure determination with high accuracy and ultra-fast speed Jue Wang, Cheng Tan, Zhangyan framework figure

End-to-end cryo-EM complex structure determination with high accuracy and ultra-fast speed
Jue Wang, Cheng Tan, Zhangyang Gao, Guijun Zhang, Yang Zhang, Stan Z. Li
Nature Machine Intelligence, 2025 [DOI]

R3Design: deep tertiary structure-based RNA sequence design and beyond Cheng Tan, Yijie Zhang, Zhangyang Gao, Hanqun Cao framework figure

R3Design: deep tertiary structure-based RNA sequence design and beyond
Cheng Tan, Yijie Zhang, Zhangyang Gao, Hanqun Cao, Siyuan Li, Siqi Ma, Mathieu Blanchette, Stan Z. Li
Briefings in Bioinformatics, 2025 [PMC] [DOI]

An Extensive Survey With Empirical Studies on Deep Temporal Point Process Haitao Lin, Cheng Tan, Lirong Wu, Zicheng Liu, framework figure

An Extensive Survey With Empirical Studies on Deep Temporal Point Process
Haitao Lin, Cheng Tan, Lirong Wu, Zicheng Liu, Zhangyang Gao, Stan Z. Li
TKDE, 2025 [PDF] [DOI]

G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction Via Evolutionary Diffusion Mengdi Liu, Zhangyang Gao, Hong framework figure

G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction Via Evolutionary Diffusion
Mengdi Liu, Zhangyang Gao, Hong Chang, Stan Z. Li, Shiguang Shan, Xilin Chen
ICCV, 2025 [PDF] [DOI]

From Words to Structured Visuals: A Benchmark and Framework for Text-to-Diagram Generation and Editing Jingxuan Wei, Che framework figure

From Words to Structured Visuals: A Benchmark and Framework for Text-to-Diagram Generation and Editing
Jingxuan Wei, Cheng Tan, Qi Chen, Gaowei Wu, Siyuan Li, Zhangyang Gao, Linzhuang Sun, Bihui Yu, Ruifeng Guo
CVPR, 2025 [PDF] [Page]

SketchAgent: Generating Structured Diagrams from Hand-Drawn Sketches Cheng Tan, Qi Chen, Jingxuan Wei, Gaowei Wu, Zhangy framework figure

SketchAgent: Generating Structured Diagrams from Hand-Drawn Sketches
Cheng Tan, Qi Chen, Jingxuan Wei, Gaowei Wu, Zhangyang Gao, Siyuan Li, Bihui Yu, Ruifeng Guo, Stan Z. Li
IJCAI, 2025 [PDF] [DOI]

EVA: Geometric Inverse Design for Fast Protein Motif-Scaffolding with Coupled Flow Yufei Huang, Yunshu Liu, Lirong Wu, H framework figure

EVA: Geometric Inverse Design for Fast Protein Motif-Scaffolding with Coupled Flow
Yufei Huang, Yunshu Liu, Lirong Wu, Haitao Lin, Cheng Tan, Odin Zhang, Zhangyang Gao, Siyuan Li, Zicheng Liu, Yunfan Liu, Tailin Wu, Stan Z. Li
ICLR, 2025 [PDF] [OpenReview]

MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction Cheng Tan, Zhenxiao Cao, Zhan framework figure

MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction
Cheng Tan, Zhenxiao Cao, Zhangyang Gao, Lirong Wu, Siyuan Li, Yufei Huang, Jun Xia, Bozhen Hu, Stan Z. Li
ICLR, 2025 [PDF] [OpenReview]

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing Shaorong Chen, Jun Xia, Jingbo Zhou, Lecheng Zhang, framework figure

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing
Shaorong Chen, Jun Xia, Jingbo Zhou, Lecheng Zhang, Zhangyang Gao, Bozhen Hu, Cheng Tan, Wenjie Du, Stan Z. Li
ICLR, 2025 [PDF] [OpenReview]

PFMBench: Protein Foundation Model Benchmark Zhangyang Gao, Hao Wang, Cheng Tan, Chenrui Xu, Mengdi Liu, Bozhen Hu, Linl framework figure

PFMBench: Protein Foundation Model Benchmark
Zhangyang Gao, Hao Wang, Cheng Tan, Chenrui Xu, Mengdi Liu, Bozhen Hu, Linlin Chao, Xiaoming Zhang, Stan Z. Li
arXiv, 2025 [PDF] [arXiv]

AlphaFold Database Debiasing for Robust Inverse Folding Cheng Tan, Zhenxiao Cao, Zhangyang Gao, Siyuan Li, Yufei Huang, framework figure

AlphaFold Database Debiasing for Robust Inverse Folding
Cheng Tan, Zhenxiao Cao, Zhangyang Gao, Siyuan Li, Yufei Huang, Stan Z. Li
NeurIPS, 2025 [PDF] [arXiv]

ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search Mengdi Liu, Xiaoxue Cheng, Zhangyang Gao, framework figure

ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
Mengdi Liu, Xiaoxue Cheng, Zhangyang Gao, Hong Chang, Cheng Tan, Shiguang Shan, Xilin Chen
NeurIPS, 2025 (Spotlight) [PDF] [arXiv]

ProteinReasoner: A Multi-Modal Protein Language Model with Chain-of-Thought Reasoning for Efficient Protein Design Chaoz framework figure

ProteinReasoner: A Multi-Modal Protein Language Model with Chain-of-Thought Reasoning for Efficient Protein Design
Chaozhong Liu, Linlin Chao, Shaomin Ji, Hao Wang, Tao Jiang, Zhangyang Gao, Yucheng Guo, Ming Yang, Xiaoming Zhang
bioRxiv, 2025 [bioRxiv] [Figure]

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design Lang Yu, Zhangyang Gao, Cheng Tan framework figure

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design
Lang Yu, Zhangyang Gao, Cheng Tan, Qin Chen, Jie Zhou, Liang He
arXiv, 2025 [PDF] [arXiv]

Lost in Tokenization: Context as the Key to Unlocking Biomolecular Understanding in Scientific LLMs Kai Zhuang, Jiawei Z framework figure

Lost in Tokenization: Context as the Key to Unlocking Biomolecular Understanding in Scientific LLMs
Kai Zhuang, Jiawei Zhang, Yumou Liu, Hanqun Cao, Chunbin Gu, Mengdi Liu, Zhangyang Gao, Zitong Jerry Wang, Xuanhe Zhou, Pheng-Ann Heng, Lijun Wu, Conghui He, Cheng Tan
arXiv, 2025 [PDF] [arXiv]

Unifying Tree Search Algorithm and Reward Design for LLM Reasoning: A Survey Jiaqi Wei, Xiang Zhang, Yuejin Yang, Wenxua framework figure

Unifying Tree Search Algorithm and Reward Design for LLM Reasoning: A Survey
Jiaqi Wei, Xiang Zhang, Yuejin Yang, Wenxuan Huang, Juntai Cao, Sheng Xu, Xiang Zhuang, Zhangyang Gao, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Chenyu You, Wanli Ouyang, Siqi Sun
arXiv, 2025 [PDF] [arXiv]

From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery Jiaqi Wei, Yuejin Yang, Xiang Zhang, framework figure

From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery
Jiaqi Wei, Yuejin Yang, Xiang Zhang, Yuhan Chen, Xiang Zhuang, Zhangyang Gao, Dongzhan Zhou, Guangshuai Wang, Zhiqiang Gao, Juntai Cao, Zijie Qiu, Xuming He, Qiang Zhang, Chenyu You, Shuangjia Zheng, Ning Ding, Wanli Ouyang, Nanqing Dong, Yu Cheng, Siqi Sun, Lei Bai, Bowen Zhou
arXiv, 2025 [PDF] [arXiv]

UniIF: Unified Molecule Inverse Folding
Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu, Yufei Huang, Siyuan Li, Zhirui Ye, Stan Z. Li
NeurIPS, 2024
[PDF] [Github] [BibTeX]

A Graph is Worth $ K $ Words: Euclideanizing Graph using Pure Transformer
Zhangyang Gao, Daize Dong, Cheng Tan, Jun Xia, Bozhen Hu, Stan Z.Li
ICML, 2024
[PDF] [Github] [BibTeX]

KW-Design: Pushing the Limit of Protein Design via Knowledge Refinement
Zhangyang Gao, Cheng Tan, Xingran Chen, Yijie Zhang, Jun Xia, Siyuan Li, Stan Z.Li
ICLR, 2024
[PDF] [Github] [BibTeX]

Proteininvbench: Benchmarking protein inverse folding on diverse tasks, models, and metrics
Zhangyang Gao, Cheng Tan, Yijie Zhang, Xingran Chen, Lirong Wu, Stan Z.Li
NeurIPS, 2023
[PDF] [Github] [BibTeX]

PiFold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, Stan Z.Li
ICLR, 2023 (Spotlight)
[PDF] [Github] [BibTeX]

Simvp: Simpler yet better video prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z.Li
CVPR, 2022
[PDF] [Github] [BibTeX]

Foldtoken: Learning protein language via vector quantization and beyond
Zhangyang Gao, Cheng Tan, Jue Wang, Yufei Huang, Lirong Wu, Stan Z.Li
AAAI, 2025
[PDF] [BibTeX] FoldToken2: Learning compact, invariant and generative protein structure language
Zhangyang Gao, Cheng Tan, Stan Z.Li
Arxiv, 2024
[PDF] [Github] [BibTeX] FoldToken3: Fold Structures Worth 256 Words or Less
Zhangyang Gao, Cheng Tan, Stan Z.Li
Arxiv, 2024
[PDF] [BibTeX] FoldToken4: Consistent & Hierarchical Fold Language
Zhangyang Gao, Cheng Tan, Stan Z.Li
Arxiv, 2024
[PDF] [BibTeX]

General Point Model Pretraining with Autoencoding and Autoregressive
Zhe Li, Zhangyang Gao*, Cheng Tan, Bocheng Ren, Laurence T Yang, Stan Z.
CVPR, 2024
[PDF] [BibTeX]

dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
Cheng Tan, Yijie Zhang, Zhangyang Gao, Yufei Huang, Haitao Lin, Lirong Wu, Fandi Wu, Mathieu Blanchette, Stan Z. Li
AAAI, 2025 (Oral)
[PDF] [BibTeX]

Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization
Lirong Wu, Haitao Lin, Yufei Huang, Zhangyang Gao, Cheng Tan, Yunfan Liu, Tailin Wu, Stan Z. Li
AAAI, 2025
[PDF] [BibTeX]

Cross-Gate MLP with Protein Complex Invariant Embedding Is a One-Shot Antibody Designer
Cheng Tan, Zhangyang Gao*, Lirong Wu, Jun Xia, Jiangbin Zheng, Xihong Yang, Yue Liu, Bozhen Hu, Stan Z.
AAAI, 2024
[PDF] [Github] [BibTeX]

Co-supervised Pre-training of Pocket and Ligand
Zhangyang Gao, Cheng Tan, Jun Xia, Stan Z.
ECML, 2023
[PDF] [BibTeX]

Target-aware molecular graph generation
Cheng Tan, Zhangyang Gao*, Stan Z.
ECML, 2023
[PDF] [BibTeX]

Global-context aware generative protein design
Cheng Tan, Zhangyang Gao*, Jun Xia, Bozhen Hu, Stan Z.
ICASSP, 2023
[PDF] [Github] [BibTeX]

Temporal attention unit: Towards efficient spatiotemporal predictive learning
Cheng Tan, Zhangyang Gao*, Lirong Wu, Yongjie Xu, Jun Xia, Siyuan Li and Li, Stan Z.
CVPR, 2023
[PDF] [Github] [BibTeX]

Conditional local convolution for spatio-temporal meteorological forecasting
Haitao Lin, Zhangyang Gao*, Yongjie Xu, Ling Li, Stan Z.Li
AAAI, 2021
[PDF] [Github] [BibTeX]

Hyperspherical consistency regularization
Cheng Tan, Zhangyang Gao*, Lirong Wu, Siyuan Li, Stan Z.Li
CVPR, 2022
[PDF] [Github] [BibTeX]

Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases
Jun Xia, Shaorong Chen, Jingbo Zhou, Xiaojun Shan, Wenjie Du, Zhangyang Gao, Cheng Tan, Bozhen Hu, Jiangbin Zheng, Stan Z. Li
NeurIPS, 2024
[PDF] [BibTeX]

ProtGO: Function-Guided Protein Modeling for Unified Representation Learning
Bozhen Hu, Cheng Tan, Yongjie Xu, Zhangyang Gao, Jun Xia, Lirong Wu, Stan Z. Li
NeurIPS, 2024
[PDF] [BibTeX]

DiscoGNN: A Sample-Efficient Framework for Self-Supervised Graph Representation Learning
Jun Xia, Shaorong Chen, Yue Liu, Zhangyang Gao, Jiangbin Zheng, Xihong Yang, Stan.Z
ICDE, 2024
[PDF] [BibTeX]

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction
Lirong Wu, Yufei Huang, Chen Tan, Zhangyang Gao, Bozhen Hu, Haitao Lin, Zicheng Liu, Stan.Z
AAAI, 2024
[PDF] [Github] [BibTeX]

Enhancing human-like multimodal reasoning: a new challenging dataset and comprehensive framework
Jingxuan Wei, Cheng Tan, Zhangyang Gao, Linzhuang Sun, Siyuan Li, Bihui Yu, Ruifeng Guo & Stan.Z
Neural Computing and Applications, 2024
[PDF] [BibTeX]

Protein 3D Graph Structure Learning for Robust Structure-Based Protein Property Prediction
Yufei Huang, Siyuan Li, Lirong Wu, Jin Su, Haitao Lin, Odin Zhang, Zihan Liu, Zhangyang Gao, Jiangbin Zheng, Stan.Z
AAAI, 2024
[PDF] [BibTeX]

A Teacher-Free Graph Knowledge Distillation Framework With Dual Self-Distillation
Lirong Wu, Haitao Lin, Zhangyang Gao, Guojiang Zhao, Stan.Z
TNNLS, 2024
[PDF] [Github] [BibTeX]

Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang Gao, Siyuan Li, Stan.Z
ICML, 2024 (Spotlight)
[PDF] [BibTeX]

Mlip: Enhancing medical visual representation with divergence encoder and knowledge-guided contrastive learning
Zhe Li, Laurence T. Yang, Bocheng Ren, Xin Nie, Zhangyang Gao, Cheng Tan, Stan Z.
CVPR, 2024
[PDF] [BibTeX]

A Survey on Generative Diffusion Models
Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, Stan Z.
TKDE, 2024
[PDF] [Github] [BibTeX]

Openstl: A comprehensive benchmark of spatio-temporal predictive learning
Cheng Tan, Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng Liu, Lirong Wu, Stan Z.Li
NeurIPS, 2023
[PDF] [Github] [BibTeX]

Mole-bert: Rethinking pre-training graph neural networks for molecules
Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, Stan Z.Li
ICLR, 2023
[PDF] [Github] [BibTeX]

Beyond homophily and homogeneity assumption: Relation-based frequency adaptive graph neural networks
Lirong Wu, Haitao Lin, Bozhen Hu, Cheng Tan, Zhangyang Gao, Zicheng Liu, Stan Z.Li
TNNLS, 2023
[PDF] [Github] [BibTeX]

Self-supervised learning on graphs: Contrastive, generative, or predictive
Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, Stan Z.Li
TKDE, 2021
[PDF] [Github] [BibTeX]

Services

Area Chair

Conference and Workshop on Neural Information Processing Systems (NeurIPS)

Program committee member | Reviewer

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
International Conference on Computer Vision (ICCV)
International Conference on Learning Representations (ICLR)
Conference and Workshop on Neural Information Processing Systems (NeurIPS)
International Conference on Machine Learning (ICML)
Association for the Advancement of Artificial Intelligence (AAAI)