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何亮
北京中关村学院导师
何亮博士，中关村学院研究员，中国科学技术大学计算机专业学士和博士。他曾任前微软亚洲研究院和微软科学智能研究院 (Microsoft Research AI for Science) 高级研究员。研究方向涵盖生物基础模型及应用、突变效应预测、计算生物学和机器学习。曾在 Nature Machine Intelligence、Nature Communications、Genome Biology、 Briefings in Bioinformatics、IEEE Transaction on Big Data、VLDB、NeurIPS、ICLR、KDD、ACL、SIGIR、RecSys等顶级期刊与会议上发表多篇论文。他是中国电子工业出版社出版《知识图谱：概念与技术》的作者之一，微软开源项目 Microsoft Graph Engine 的核心贡献者之一，开发的系统曾为微软Bing、Xbox等全球线上产品提供实时服务，并主导研发了强类型 RDF 引擎 Stylus。现为Nature Machine Intelligence等期刊审稿人。
I. 研究方向
- 生物大模型及应用
- 计算生物学及应用
II. 教育经历
- 中国科学技术大学，本科
- 中国科学技术大学，硕士及博士
III. 工作经历
- 2018年-2022年，微软亚洲研究员，副研究员、高级研究员
- 2022年-2025年，微软科学智能研究院，高级研究员
IV. 代表性学术论文
- Jianwei Zhu, Yu Shi, Ran Bi, Peiran Jin, Chang Liu, Zhe Zhang, Haitao Huang, Zekun Guo, Pipi Hu, Fusong Ju, Lin Huang, Xinwei Tai, Chenao Li, Kaiyuan Gao, Xinran Wei, Huanhuan Xia, Jia Zhang, Yaosen Min, Zun Wang, Yusong Wang, Liang He, Haiguang Liu, Tao Qin. FlexProtein: Joint Sequence and Structure Pretraining for Protein Modeling. ICLR, 2026.
- Chuan Cao*#, Liang He*#, Chengping Li*, Yuliang Jiang*, Chuyue Tang*, Chengyue Huang*, Yuman Li*, Yuan He, Yaosen Min, Haiguang Liu, Tao Qin, Tie-Yan Liu. Illuminating the Virosphere’s Dark Matter using Hierarchical Deep Learning. bioRxiv, 2025.
- Weijie Yin*, Zhaoyu Zhang*, Liang He*, Rui Jiang, Shuo Zhang, Gan Liu, Xuegong Zhang#, Tao Qin#, Zhen Xie#. ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations. Nature Communications, 2025.
- Haoran Sun*, Liang He*#, Pan Deng*#, Guoqing Liu*, Zhiyu Zhao, Yuliang Jiang, Chuan Cao, Fusong Ju, Lijun Wu, Haiguang Liu#, Tao Qin#, Tie-Yan Liu. Accelerating protein engineering with fitness landscape modeling and reinforcement learning. Nature Machine Intelligence, 2025.
- Yangzhe Peng*, Kaiyuan Gao*, Liang He, Yuheng Cong, Haiguang Liu, Kun He, Lijun Wu. CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions. KDD, 2025.
- Yingce Xia*, Peiran Jin*, Shufang Xie*, Liang He*, Chuan Cao*, Renqian Luo*, Guoqing Liu*, Yue Wang*, Zequn Liu*, Yuan-Jyue Chen*, Zekun Guo*, et al. NatureLM: Deciphering the Language of Nature for Scientific Discovery. arXiv, 2025.
- Liang He*, Peiran Jin*, Yaosen Min*, Shufang Xie*, Lijun Wu*, Tao Qin*, Xiaozhuan Liang, Kaiyuan Gao, Yuliang Jiang, Tie-Yan Liu. SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation. arXiv, 2024.
- Microsoft Research AI4Science and Microsoft Azure Quantum. The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4. arXiv, 2023.
- Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu. Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design. KDD 2023.
- Yongge Li, Fusong Ju, Zhiyuan Chen, Yiming Qu, Huanhuan Xia, Liang He, Lijun Wu, Jianwei Zhu, Bin Shao, Pan Deng. CREaTor: Zero-shot cis-regulatory pattern modeling with attention mechanisms. Genome Biology, 2023.
- Lijun Wu, Chengcan Ying, Jinhua Zhu, Zheng Wu, Liang He, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu. SPRoBERTa: Protein Embedding Learning with Local Fragment Modeling. Briefings in Bioinformatics, 2022.
- Siyuan Liu, Yusong Wang, Tong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu. Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer. Briefings in Bioinformatics, 2022.
- Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, Bin Shao, Tao Qin, Tie-Yan Liu. Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model. arXiv, 2021.
- He Zhang*, Fusong Ju*, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu. Co-evolution Transformer for Protein Contact Prediction. NeurIPS, 2021.
- Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu. SEEK: Segmented Embedding of Knowledge Graphs. ACL, 2020.
- Jingping Liu, Yanghua Xiao, Ao Wang, Liang He, Bin Shao. CapableOf reasoning: A step towards commonsense oracle. SIGIR 2020.
- Liang He, Bin Shao, Yatao Li, Huanhuan Xia, Yanghua Xiao, Enhong Chen, Liang Jeff Chen. Stylus: A Strongly-Typed Store for Serving Massive RDF Data. VLDB, 2018.
- Liang He, Bin Shao, Yanghua Xiao, Yatao Li, Tie-Yan Liu, Enhong Chen, Huanhuan Xia. Neurally-Guided Semantic Navigation in Knowledge Graph. IEEE Transactions on Big Data, 2018.
- Yi Zheng, Qi Liu, Enhong Chen, J. Leon Zhao, Liang He, and Guangyi Lv. Convolutional Nonlinear Neighbourhood Components Analysis for Time Series Classification, PAKDD, 2015.
- Xiang Wu, Qi Liu, Enhong Chen, Liang He, Jingsong Lv, Can Cao, Guoping Hu, Personalized Next-song Recommendation in Online Karaokes. RecSys, 2013.
- Qi Liu, Enhong Chen, Biao Xiang, Chris H. Q. Ding, Liang He, Gaussian Process for Recommender Systems. KSEM, 2011.
V. 学术兼职
- Nature Machine Intelligence审稿人
- Transactions on Machine Learning Research审稿人