(叶启威)
Vice President / Tech Lead Beijing Academy of Artificial Intelligence (BAAI)
Leads the Health Computing Research Center at BAAI, where he created OpenComplex. Before BAAI, he spent nearly eight years at Microsoft Research Asia, co-creating LightGBM and Suphx. His work spans generative AI, reinforcement learning, and AI for Science.
Introduced AREX — a family of recursively self-improving deep research agents that exploit discovery–verification asymmetry. AREX alternates between an inner research loop and an outer self-improvement loop that audits answers constraint-wise. With a 122B-A10B MoE model, it substantially outperforms comparable-scale baselines across BrowseComp, WideSearch, DeepSearchQA, and HLE.
Floyd-ARC, built on FloydNet, scores 70.5 on ARC-AGI-1 with only 154M parameters — surpassing Grok-4-thinking (1.7T) and setting a new SOTA among ARC-trained models.
Launched the OpenComplex Server — OpenComplex2 (OC2) goes beyond static structure prediction, generating full conformational ensembles to capture the dynamic nature of biomolecules.
IneqSearch accepted at NeurIPS 2025 — solving 78.3% of 437 Olympiad-level inequalities via hybrid symbolic-neural reasoning, over 100x faster than prior SOTA.
UltraSelex — a single-step approach to high-affinity RNA ligand discovery, in collaboration with Heidelberg. Cover article in Nature Chemical Biology.
OpenComplex held the top position on CAMEO protein structure prediction for nearly 30 consecutive months.
Released Suphx — the first AI to reach superhuman level in Mahjong through deep reinforcement learning.
Released LightGBM at NeurIPS 2017 — now among the most widely cited machine learning papers, with 27,900+ citations.
For a full list, please visit Google Scholar
Shuqi Lu*, Chaofan Li*, Kun Luo*, Zhang Zhang*, Hui Wang*, Hongwang Xiao*, Zheng Liu*, Lei Xiong, Jiahao Wang, Sen Wang, Xiyan Jiang, Wanli Li, Yuyang Hu, Hongjin Qian, Bingyu Yan, Ziyi Xia, Yingxia Shao, Kang Liu, Zhicheng Dou, Di He, Chaozhuo Li, Qiwei Ye, Zhongyuan Wang
arXiv 2026
Jingcheng Yu, Mingliang Zeng, Qiwei Ye
arXiv 2026
Zhaoqun Li, Beishui Liao, Qiwei Ye
NeurIPS 2025
Yaqing Zhang, Yuan Jiang, David Kuster, Qiwei Ye, Wenhao Huang, Simon Fürbacher, Jingye Zhang, Pia Doll, Wenjun Lin, Siwei Dong, Hui Wang, Zhipeng Tang, David Ibberson, Klemens Wild, Irmgard Sinning, Anthony A. Hyman, Andres Jäschke
Nature Chemical Biology 2025
OpenComplex Team, Qiwei Ye
bioRxiv 2025
Peitian Zhang, Zheng Liu, Shitao Xiao, Ninglu Shao, Qiwei Ye, Zhicheng Dou
arXiv 2024
Bohang Zhang, Jingchu Gai, Yiheng Du, Qiwei Ye, Di He, Liwei Wang
ICLR 2024 Outstanding Paper Honorable Mention
Yuyan Ni, Shikun Feng, Xin Hong, Yuancheng Sun, Wei-Ying Ma, Zhi-Ming Ma, Qiwei Ye, Yanyan Lan
Nature Machine Intelligence 2024
Daiwen Sun, He Huang, Yao Li, Xinqi Gong, Qiwei Ye
NeurIPS 2023
Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, Hsiao-Wuen Hon
arXiv 2020
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu
NeurIPS 2017
How do machines learn from experience, make decisions under uncertainty, and transfer knowledge to unfamiliar problems? We study learning, reasoning, and generalization to uncover computational principles shared across tasks.
Bringing learning and reasoning into contact with the physical world. We explore foundation models that capture structure, dynamics, and interactions across scales, combining data with physical constraints to predict how complex systems behave.
Living systems connect molecular interactions to processes in cells, tissues, and organisms. We explore how generative models can bring together multi-omics data and information across scales to reveal relationships between structure, function, and dynamics, and open up new avenues for biological discovery.
Dec 2021 – Present
Beijing Academy of Artificial Intelligence (BAAI)
Building OpenComplex for biomolecular structure prediction, and exploring the intersection of foundation models and life sciences.
2021 – 2025
Tsinghua AIR–BAAI Joint Research Center for Health
Mar 2014 – Dec 2021
Microsoft Research Asia
Co-created LightGBM and Suphx. Research in gradient boosting, reinforcement learning, and multi-agent systems.
2012 – 2015
Peking University