About Me

Hi, I am an Assistant Professor at Zhongguancun Academy. Prior to this, I received my Ph.D. degree in Computer Science from Zhejiang University, under the supervision of Prof. Jian Wu, Prof. Tingjun Hou, and Prof. Chang-Yu Hsieh.

My research interests lie in Generative AI (e.g., diffusion models and multimodal large language models) and AI for Science & Health, with a particular focus on drug discovery, scientific foundation models, and sports. My work has been published in leading conferences and journals, including NeurIPS, ICLR, SIGKDD, and Advanced Science, among others. I also serve as a reviewer for several prestigious venues such as Nature Machine Intelligence, Nature Computational Science, NeurIPS, ICLR, and ICML.

I am seeking highly motivated Ph.D. students with strong backgrounds in machine learning, generative modeling, or AI for Science to pursue cutting-edge research at Zhongguancun Academy. Interested candidates are encouraged to contact me via email with their CV and research interests.

🎺 I am particularly excited about AI for Sports and Health, including sports video understanding, exercise for health, wearable intelligence, and personalized health management. Students interested in these topics, especially those working on multimodal LLMs and agents, are very welcome to reach out!

🔥 News

• [2026.09] Two papers have been accepted to NeurIPS 2026. Congratulations to Shikun and Junan!
• [2026.08] One paper has been accepted to Findings of EMNLP 2026. Congratulations to Mingze!
• [2026.06] One paper has been accepted to ECCV 2026. Congratulations to Xiaoye!
• [2026.05] Our paper on protein language models has been accepted to KDD 2026 AI4Sciences Track. Congratulations to Mingze!
• [2026.05] Our survey on controllable protein sequence design has been accepted by npj Drug Discovery.

📄 Selected Publications

The full list can be accessed on my Google Scholar profile.

• [npj Drug Discovery 2026] Generative AI for controllable protein sequence design: A survey
  Yiheng Zhu, Zitai Kong, Jialu Wu, Mingze Yin, Weize Liu, Yuqiang Han, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou, Jian Wu

• [Advanced Science 2025] A Multi-Objective Molecular Generation Method Based on Pareto Algorithm and Monte Carlo Tree Search
  Yifei Liu†, Yiheng Zhu†, Jike Wang, Renling Hu, Chao Shen, Wanglin Qu, Gaoang Wang, Qun Su, Yuchen Zhu, Yu Kang, Peichen Pan, Chang‐Yu Hsieh, Tingjun Hou

• [JACS Au 2025] HiCLR: Knowledge-Induced Hierarchical Contrastive Learning with Retrosynthesis Prediction Yields a Reaction Foundation Model [Code]
  Jialu Wu†, Yiheng Zhu†, Xiaorui Wang, Yitong Li, Mingze Yin, Tianyue Wang, Yuqiang Han, Yu Kang, Yafeng Deng, Jian Wu, Chang-Yu Hsieh, Tingjun Hou

• [AAAI 2025] Synergy of GFlowNet and Protein Language Model Makes a Diverse Antibody Designer
  Mingze Yin†, Hanjing Zhou†, Yiheng Zhu†, Jialu Wu, Wei Wu, Mingyang Li, Kun Fu, Zheng Wang, Chang-Yu Hsieh, Tingjun Hou, Jian Wu

• [NeurIPS 2024] Bridge-IF: Learning Inverse Protein Folding with Markov Bridges [Code]
  Yiheng Zhu, Jialu Wu, Qiuyi Li, Jiahuan Yan, Mingze Yin, Wei Wu, Mingyang Li, Jieping Ye, Zheng Wang, Jian Wu

• [Health Data Science 2024] Multi-Modal CLIP-Informed Protein Editing [Code]
  Mingze Yin†, Hanjing Zhou†, Yiheng Zhu†, Miao Lin, Yixuan Wu, Jialu Wu, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou, Jintai Chen, Jian Wu

• [NeurIPS 2023] Sample-efficient Multi-objective Molecular Optimization with GFlowNets [Code]
  Yiheng Zhu†, Jialu Wu†, Chaowen Hu, Jiahuan Yan, Chang-Yu Hsieh, Tingjun Hou, Jian Wu

• [IJCAI 2023] MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation [Code]
  Yiheng Zhu†, Zhenqiu Ouyang†, Ben Liao, Jialu Wu, Yixuan Wu, Chang-Yu Hsieh, Tingjun Hou, Jian Wu

• [EMNLP 2026 Findings] Func-R1: Incentivizing Mathematical Function Reasoning in Multimodal Large Language Models
  Mingze Yin, Xiaohan Wang, Dian Li, Yao Haichao, Yilin Zhao, Youjun Chen, sinbadliu, Jintai Chen, Yiheng Zhu*, Chang-Yu Hsieh*, Aimin PAN*

• [ECCV 2026] VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-On [Code]
  Xiaoye Liang, Zhiyuan Qu, Mingye Zou, Jiaxin Liu, Lai Jiang, Mai Xu, Yiheng Zhu*

• [SIGKDD 2026] Caduceus: MoE Foundation Models for Unifying Biological and Natural Language
  Mingze Yin†, Yiheng Zhu†, Jialu Wu, Jian Ma, Hanjing Zhou, Mingyang Li, Yuhua Zhou, Jintai Chen, Tingjun Hou, Jieping Ye, Aimin Pan

• [ICLR 2024] Making Pre-trained Language Models Great on Tabular Prediction [Code]
  Jiahuan Yan, Bo Zheng, Hongxia Xu, Yiheng Zhu, Danny Z Chen, Jimeng Sun, Jian Wu, Jintai Chen

• [arXiv] GENERator: A Long-Context Generative Genomic Foundation Model [Code]
  Wei Wu, Qiuyi Li, Yuanyuan Zhang, Zhihao Zhan, Ruipu Chen, Mingyang Li, Kun Fu, Junyan Qi, Yongzhou Bao, Chao Wang, Yiheng Zhu, Zhiyun Zhang, Jian Tang, Fuli Feng, Jieping Ye, Yuwen Liu, Hui Xiong, Zheng Wang

• [bioRxiv] Functional In-Context Learning in Genomic Language Models with Nucleotide-Level Supervision and Genome Compression
  Qiuyi Li, Zhihao Zhan, Shikun Feng, Yiheng Zhu, Yuan He, Wei Wu, Zhenghang Shi, Shengjie Wang, Zongyong Hu, Zhao Yang, Jiaoyang Li, Jian Tang, Haiguang Liu, Tao Qin

• [bioRxiv] Generanno: A Genomic Foundation Model for Metagenomic Annotation [Code]
  Qiuyi Li, Wei Wu, Yiheng Zhu, Fuli Feng, Jieping Ye, Zheng Wang

• [Genome Biology 2025] Biology-driven insights into the power of single-cell foundation models [Code]
  Jialu Wu, Qing Ye, Yilin Wang, Renling Hu, Yiheng Zhu, Mingze Yin, Tianyue Wang, Jike Wang, Chang-Yu Hsieh, Tingjun Hou

• [Bioinformatics 2022] TGSA: protein–protein association-based twin graph neural networks for drug response prediction with similarity augmentation [Code]
  Yiheng Zhu†, Zhenqiu Ouyang†, Wenbo Chen, Ruiwei Feng, Danny Z Chen, Ji Cao, Jian Wu