基本信息
导师姓名：刘铁岩
性别：男
职称：教授
电子邮件：tie-yan.liu@outlook.com
任职单位：北京中关村学院院长
个人经历
教育背景：
1994-1998 | 清华大学 | 学士 |
1998-2003 | 清华大学 | 博士 |
工作简历：
2003-2024 | 微软研究院 | 杰出首席科学家 |
2024- | 北京中关村学院 | 院长 |
社会兼职：
2018-今 | 清华大学兼职教授 |
2012-今 | 中国科学技术大学兼职教授 |
2021-今 | 华中科技大学 兼职教授 |
2014-2022 | CMU客座教授 |
科学研究
研究领域：促进了机器学习与信息检索的融合，被公认为“排序学习”领域的代表人物。在深度学习、强化学习、以及人工智能驱动的科学发现等方面也颇有建树，著有两部学术专著、在顶级国际会议和国际期刊上发表了数百篇论文，包含多篇自然杂志（Nature）及其子刊的论文，学术成果至今已被引用近七万次，最高单篇论文引用超过1万3千次，H-index高达97。曾担任诸多人工智能顶级会议（如WWW/WebConf、SIGIR、NeurIPS、ICLR、ICML、IJCAI、AAAI、KDD等）的大会主席、程序委员会主席、或（资深）领域主席，以及ACM TOIS、ACM TWEB、IEEE TPAMI等国际知名期刊副主编。
近期主要学术成果：
l 2024年在Nature正刊发表突破性成果，首次实现量子化学精度的全原子蛋白质大分子动力学模拟。
l 2023年发布了TamGen模型，与全球健康药物研发中心（GHDDI）合作，为肺结核和冠状病毒等肆虐全球的传染病设计出全新的高效候选药物，实验室合成和实验表面：与已知先导化合物相比，其生物活性提高了近10倍。
l 2022年发布了VisNet模型，将人工智能成功应用于蛋白质大分子动力学模拟，在首届AI药物研发算法大赛中获得冠军。
l 2022年发布了BioGPT模型，在科学文献的理解能力上显著超越其他大语言模型，在生物医学领域的PubMed问答任务上首次达到人类专家水平。
l 2021年发布了用于分子建模的Graphormer算法，单篇论文引用千余次，并在首届 OGB-LSC 分子建模国际比赛和 OC20 催化剂设计国际开放挑战赛中获得冠军。
l 2019年发布了麻将AI Suphx，在国际知名竞技麻将平台“天凤”上首次荣升十段，稳定段位显著超越人类顶级选手，在竞技麻将界引起很大反响，有两本著作专门讨论和分析Suphx的战略战术。
l 2018年发明了对偶学习技术，单篇论文被引用千余次，在中英新闻翻译任务上首次达到了人类专家水平，并于次年获得国际机器翻译比赛的8项冠军，目前是微软Azure AI机器翻译服务的核心技术。
l 2016年发表了LightGBM算法并于次年开源，单篇论文被引用超过一万三千次; LightGBM已成为Kaggle比赛、KDD Cup和产业决策中最常用的AI工具之一，并被国际开放测评委员会评为AI领域发展至今最重要的百项研究成果之一。
奖励信息：
l 2023年 1940年以来人工智能领域最重要的百位学者（国际开放测评委员会）
l 2022年 亚太人工智能学会 会士（AAIA Fellow）
l 2022年 国际数据挖掘大会 最佳论文提名奖
l 2022年 人工智能领军人物称号
l 2021年 国际计算机学会 会士（ACM Fellow）
l 2020年 CSDN技术影响力之星
l 2019年 机器之心 最佳AI应用案例
l 2018 年 中国AI英雄风云榜 – 技术创新人物奖
l 2018年 亚洲机器学习大会 最佳学生论文奖
l 2018年 中国计算机学会 青竹奖
l 2007-2017年 Aminer 全球最具影响力学者（信息检索）
l 2016年 国际电气电子工程师学会 会士（IEEE Fellow）
l Elsevier最高引中国学者奖
l Springer最畅销科技书华人作者
l 2008年 国际信息检索大会 最佳学生论文奖
l 2004年-2006年 视觉通信与图像表达期刊 最高引用论文奖
专利成果：近百项国际专利
出版信息：
学术专著
l Tie-Yan Liu. Learning to Rank for Information Retrieval, Springer, 2011.
l 刘铁岩, 陈薇, 王太峰, 高飞，分布式机器学习：理论、算法、与系统, 机械工业出版社, 2018.
近3年代表性论文
[国际期刊]
[1] Tong Wang, Xinheng He, Mingyu Li, Yatao Li, Ran Bi, Yusong Wang, Chaoran Cheng, Xiangzhen Shen, Jiawei Meng, He Zhang, Haiguang Liu, Zun Wang, Shaoning Li, Bin Shao and Tie-Yan Liu， Ab initio characterization of protein molecular dynamics with AI2BMD, Nature, 2024.
[2] Kehan Wu, Yingce Xia, Pan Deng, Renhe Liu, Yuan Zhang, Han Guo, Yumeng Cui, Qizhi Pei, Lijun Wu, Shufang Xie, Si Chen, Xi Lu, Song Hu, Jinzhi Wu, Chi-Kin Chan, Shawn Chen, Liangliang Zhou, Nenghai Yu, Enhong Chen, Haiguang Liu, Jinjiang Guo, Tao Qin, Tie-Yan Liu, Target-aware Molecule Generation for Drug Design Using a Chemical Language Model, Nature Communications, 2024.
[3] Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu, Predicting equilibrium distributions for molecular systems with deep learning, Nature Machine Intelligence, 2024.
[4] Congqiao Li, Huilin Qu, Sitian Qian, Qi Meng, Shiqi Gong, Jue Zhang, Tie-Yan Liu, Qiang Li, Does Lorentz-symmetric design boost network performance in jet physics? Physical Review D, 2024.
[5] Juntao Li, Xiaobo Liang, Lijun Wu, Yue Wang, Qi Meng, Tao Qin, Min Zhang, Tie-Yan Liu, Randomness Regularization with Simple Consistency Training for Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
[6] Xu Tan, Jiawei Chen, Haohe Liu, Jian Cong, Chen Zhang, Yanqing Liu, Xi Wang, Yichong Leng, Yuanhao Yi, Lei He, Frank Soong, Tao Qin, Sheng Zhao, Tie-Yan Liu, Naturalspeech: End-to-end text-to-speech synthesis with human-level quality, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
[7] Tong Wang, Yusong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao, and Tie-Yan Liu, Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing, Nature Communications, 2023 (Editor’s highlights).
[8] Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Veličković, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, and Marinka Zitnik, Scientific Discovery in the Age of Artificial Intelligence, Nature, 2023.
[9] Tong Wang, Xinheng He, Mingyu Li, Bin Shao, Tie-Yan Liu, AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics, Scientific Data, 2023.
[10] Zun Wang, Hongfei Wu, Lixin Sun, Xinheng He, Zhirong Liu, Bin Shao, Tong Wang, Tie-Yan Liu, Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics, The Journal of Chemical Physics, 2023.
[11] Shiqi Gong, Xinheng He, Qi Meng, Zhiming Ma, Bin Shao, Tong Wang, Tie-Yan Liu, Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics, Journal of Physical Chemistry, 2022 (Cover page article).
[12] Rui Zhang, Peiyan Hu, Qi Meng, Yue Wang, Rongchan Zhu, Bingguang Chen, Zhi-Ming Ma, and Tie-Yan Liu, DRVN (Deep Random Vortex Network): A New Physics-informed Machine Learning Method for Simulating and Inferring Incompressible Fluid Flows, Physics of Fluids, 2022.
[13] Yutai Hou, Yingce Xia, Lijun Wu, Shufang Xie, Yang Fan, Jinhua Zhu, Wanxiang Che, Tao Qin, Tie-Yan Liu, Discovering Drug-Target Interaction Knowledge from Biomedical Literature, Bioinformatics, 2022.
[14] Shiqi Gong, Qi Meng, Jue Zhang, Huilin Qu, Congqiao Li, Sitian Qian, Weitao Du, Zhi-Ming Ma, Tie-Yan Liu, An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging, Journal of High Energy Physics, 2022.
[15] Jia Xing, Siwei Li, Shuxin Zheng, Chang Liu, Xiaochun Wang, Lin Huang, Ge Song, Yihan He, Shuxiao Wang, Shovan Kumar Sahu, Jia Zhang, Jiang Bian, Yun Zhu, Tie-Yan Liu, Jiming Hao. Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder. Environmental Science & Technology, 2022.
[16] Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Tie-Yan Liu, and Houqiang Li, Masked Contrastive Representation Learning for Reinforcement Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
[17] Xinquan Wang, Jun Lan, Xinheng He, Yifei Ren, Ziyi Wang, Huan Zhou, Shilong Fan, Chenyou Zhu, Dongsheng Liu, Bin Shao, Tie-Yan Liu, Qisheng Wang, Linqi Zhang, Jiwan Ge, and Tong Wang, Structural insights into the SARS-CoV-2 Omicron RBD-ACE2 interaction, Cell Research, 2022.
[国际会议]
[1] Shengjie Luo, Yixian Xu, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Bridging Geometric States via Geometric Diffusion Bridge, NeurIPS 2024.
[2] Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, Tie-Yan Liu, Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning, NeurIPS 2024.
[3] Bohan Wang, Yushun Zhang, Huishuai Zhang, Qi Meng, Ruoyu Sun, Zhi-Ming Ma, Tie-Yan Liu, Zhi-Quan Luo, Wei Chen， Provable Adaptivity of Adam under Non-uniform Smoothness, KDD 2024.
[4] Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, GeoMFormer: A General Architecture for Geometric Molecular Representation Learning, ICML 2024.
[5] He Zhang, Chang Liu, Zun Wang, Xinran Wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu, Self-Consistency Training for Hamiltonian Prediction, ICML 2024.
[6] Xu Tan, Tao Qin, Jiang Bian, Tie-Yan Liu, Yoshua Bengio, Regeneration learning: A learning paradigm for data generation, AAAI 2024.
[7] Yusong Wang, Shaoning Li, Tong Wang, Bin Shao, Nanning Zheng, Tie-Yan Liu, Geometric Transformer with Interatomic Positional Encoding, NeurIPS 2023.
[8] Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan, FABind: Fast and Accurate Protein-Ligand Binding, NeurIPS 2023.
[9] Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Wengang Zhou, Tao Qin, Houqiang Li, Tie-Yan Liu, Dual-view Molecular Pre-training, KDD 2023.
[10] Hangting Ye, Zhining Liu, Wei Cao, Amir Mohammad Amiri, Jiang Bian, Yi Chang, Jon D. Lurie, Jim Weinstein, Tie-Yan Liu, Web-based Long-term Spine Treatment Outcome Forecasting, KDD 2023.
[11] 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.
[12] Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu, Retrosynthetic Planning with Dual Value Networks, ICML 2023.
[13] Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu, NeuralStagger: accelerating physics-constrained neural PDE solver with spatial-temporal decomposition, ICML 2023.
[14] Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, Tie-Yan Liu, De Novo Molecular Generation via Connection-aware Motif Mining, ICLR 2023.
[15] Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He, One Transformer Can Understand Both 2D & 3D Molecular Data, ICLR 2023.
[16] Jinhua Zhu, Kehan Wu, Bohan Wang, Yingce Xia, Shufang Xie, Qi Meng, Lijun Wu, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, O-GNN: incorporating ring priors into molecular modeling, ICLR 2023.
[17] Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu, Houqiang Li, Making Better Decision by Directly Planning in Continuous Control, ICLR 2023.
[18] Shiqi Gong, Yue Wang, Qi Meng, Ni Hao, Tie-Yan Liu, Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations, AAAI 2023.
[19] Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu, Quantized Training of Gradient Boosted Decision Trees, NeurIPS 2022.
[20] Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He, Your Transformer May Not be as Powerful as You Expect, NeurIPS 2022.
[21] Botao Yu, Peiling Lu, Rui Wang, Wei Hu, Xu Tan, Wei Ye, Shikun Zhang, Tao Qin, Tie-Yan Liu, Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation, NeurIPS 2022.
[22] Bohan Wang, Qi Meng, Huishuai Zhang, Ruoyu Sun, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu, Does Momentum Change the Implicit Regularization on Separable Data? NeurIPS 2022.
[23] Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu, Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret, NeurIPS 2022.
[24] Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng CHENG, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu, An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context, NeurIPS 2022.
[25] Yichong Leng, Zehua Chen, Junliang Guo, Haohe Liu, Jiawei Chen, Xu Tan, Danilo Mandic, Lei He, Xiangyang Li, Tao Qin, Sheng Zhao, Tie-Yan Liu, BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis, NeurIPS 2022.
[26] Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Tie-Yan Liu, Nanning Zheng, Bin Shao, Equivariant graph neural networks with complete local frames, ICML 2022.
[27] Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Jian Li, Analyzing and Mitigating Interference in Neural Architecture Search, ICML 2022.
[28] Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu, Supervised Off-Policy Ranking, ICML 2022.
[29] Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu, Unified 2D and 3D Pre-Training of Molecular Representations, KDD 2022.
[30] Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu, What Makes Your Data Unavailable To Deep Learning? KDD 2022.
[31] Chongchong Li, Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu, Gradient Information Matters in Policy Optimization by Back-propagating through Model, ICLR 2022.
[32] Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu, PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior, ICLR 2022.
[33] Shufang Xie, Ang Lv, Yingce Xia, Lijun Wu, Tao Qin, Tie-Yan Liu, Rui Yan, Target-Side Data Augmentation for Sequence Generation, ICLR 2022.
[34] Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu, Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality, ICLR 2022.
[35] Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu, DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting, ICLR 2022.