Portrait of Tie-Yan Liu

Tie-Yan Liu

Principal Research Manager


Tie-Yan Liu is a principal researcher of Microsoft Research Asia, leading the machine learning group. His research interests include artificial intelligence, machine learning, information retrieval, data mining, and computational economics. As a researcher in an industrial lab, Tie-Yan is making his unique contributions to the world. On one hand, many of his technologies have been transferred to Microsoft’s products and online services, such as Bing, Microsoft Advertising, and Azure. He has received many recognitions and awards in Microsoft for his significant product impacts. On the other hand, he has been actively contributing to the academic community. He is an adjunct professor at CMU and several universities in China, and an honorary professor at Nottingham University. He is frequently invited to chair or give keynote speeches at major machine learning and information retrieval conferences. He is a senior member of the IEEE and the ACM, as well as a senior member, distinguished speaker, and academic committee member of the CCF.

Tie-Yan Liu is a pioneer in machine learning for Web search and online advertising.

  • His seminal contribution to the field of learning to rank has been widely recognized (https://en.wikipedia.org/wiki/Learning_to_rank). He invented several highly impactful algorithms and theories, including the listwise approach to ranking, relational ranking, and statistical learning theory for ranking. He is an advocator of learning to rank as a self-contained research discipline – he gave the first batch of keynote speeches and tutorials, organized the first series of workshops, and wrote the very first book on this topic (among top-10 Springer computer science books written by Chinese authors). He is the creator of LETOR benchmark dataset (http://research.microsoft.com/en-us/um/beijing/projects/letor/), which has become a must-have experimental platform for the research on learning to rank. With his deep research and social efforts, learning to rank has become a fundamental technology in major search engines today, and it continues to be one of the most important directions in the related research communities.
  • He has also done impactful work on large scale machine learning. As early as in 2005, Tie-Yan has developed the largest text classifier in the world, which can categorize over 250,000 categories on 20 machines (published at SigKDD Explorations). Recently, Tie-Yan and his team developed many other large-scale machine learning tools, including the fastest and largest topic model in the world (LightLDA, with one million topics, published at WWW 2015) and the largest word embedding model. Some of these models were open-sourced in Microsoft Distributed Machine Learning Toolkit (http://www.dmtk.io/), which has attracted millions of visitors, hundreds of thousands of downloads, and thousands of stars at GitHub.
  • He has conducted innovative research on mechanism design for online advertising. In order to bridge theory and practices, he introduced many practical constraints into auction mechanism design (e.g., bounded rationality, budget constraints), and proposed a data-driven framework called “game-theoretical machine learning” for ad auction optimization. This framework learns the bounded rationality model from data, and optimizes the action parameters based on the learned model using a simulation-based framework. The framework extends algorithmic game theory due to the introduction of data, and extends machine learning by considering the strategic (non-i.i.d.) behaviors behind data generation.

Over the years, Tie-Yan and his team have been recognized as one of the global powerhouses and trendsetter in machine learning for Web search and online advertising. He and his team have contributed hundreds of high-impact papers at top conferences – a good indicator of their influence and impact. His top ten papers have been cited over 4000 times in refereed conferences and journals. He has won quite a few awards, including the best student paper award at SIGIR (2008), the most cited paper award at Journal of Visual Communications and Image Representation (2004-2006), and the research break-through award at Microsoft Research (2012). He has been invited to serve as general chair, PC chair, or area chair for a dozen of top conferences including SIGIR, WWW, NIPS, IJCAI, AAAI, ICTIR, as well as associate editor/editorial board member of ACM Transactions on Information Systems, ACM Transactions on Web, Information Retrieval Journal, and Foundations and Trends in Information Retrieval. Tie-Yan Liu and his works have been reported by many International media, including National Public Radio, CNET, MIT Technology Review, and PCTech Magazine.

Representative Publications


  1. Tie-Yan Liu. Learning to Rank for Information Retrieval, Springer, 2011.

[Journal Papers]

  1. Shuaiqiang Wang, Shanshan Huang, Tie-Yan Liu, Jun Ma, Zhumin Chen, Jari Veijalainen, Ranking-oriented Collaborative Filtering: A Listwise Approach, ACM Transactions on Information Systems, 2016
  2. Xujin Chen, Xiaodong Hu, Tie-Yan Liu, Weidong Ma, Tao Qin, Pingzhong Tang, Changjun Wang, and Bo Zheng, Efficient Mechanism Design for Online Scheduling, Journal of Artificial Intelligence Research, 2016.
  3. Chang Xu, Gang Wang, Xiaoguang Liu, Tie-Yan Liu, Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks, IEEE Transactions on Computers, 2016.
  4. Wei Chen, Tie-Yan Liu, and Xinxin Yang, Reinforcement Learning Behaviors in Sponsored Search, Applied Stochastic Models in Business and Industry, 2016.
  5. Qing Cui, Bin Gao, Jiang Bian, Hanjun Dai, and Tie-Yan Liu, KNET: A General Framework for Learning Word Embedding using Morphological Knowledge, ACM Transactions on Information Systems, 2015.
  6. Wei Wei, Bin Gao, Tie-Yan Liu, Taifeng Wang, Guohui Li, and Hang Li, A Ranking Approach on Large-scale Graph with Multi-dimensional Heterogeneous Information, IEEE Transactions on Cybernetics, 2015.
  7. Tao Qin, Wei Chen, and Tie-Yan Liu, Sponsored Search Auctions: Recent Advances and Future Directions, ACM Transactions on Intelligent Systems and Technology, 2014.
  8. Ying Zhang, Weinan Zhang, Bin Gao, Xiaojie Yuan, and Tie-Yan Liu, Bid Keyword Suggestion in Sponsored Search based on Competitiveness and Relevance, Information Processing and Management, 2014.
  9. Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, Online Learning for Auction Mechanism in Bandit Setting, Decision Support Systems, 2013
  10. Bin Gao, Tie-Yan Liu, Yuting Liu, Taifeng Wang, Zhiming Ma, and Hang Li, Page Importance Computation based on Markov Processes, Information Retrieval, 2011.
  11. Olivier Chapelle, Yi Chang, and Tie-Yan Liu, Future research directions on learning to rank, Proceeding track, Journal of Machine Learning Research, 2011.
  12. Xiubo Geng, Tie-Yan Liu, Tao Qin, Xueqi Cheng, Hang Li, Selecting Optimal Training Data for Learning to Rank, Information Processing and Management, 2011.
  13. Yin He and Tie-Yan Liu, Tendency Correlation Analysis for Direct Optimization of Evaluation Measures in Information Retrieval, Information Retrieval, 2010.
  14. Tie-Yan Liu, Thorsten Joachims, Hang Li, and Chengxiang Zhai, Introduction to special issue on learning to rank for information retrieval, Information Retrieval, 2010.
  15. Tie-Yan Liu. Learning to Rank for Information Retrieval, Foundations and Trends in Information Retrieval, 2009.
  16. Yuting Liu, Tie-Yan Liu, Zhiming Ma, and Hang Li. A Framework to Compute Page Importance based on User Behaviors, Information Retrieval, 2009.
  17. Tao Qin, Tie-Yan Liu, and Hang Li, A General Approximation Framework for Direct Optimization of Information Retrieval Measures, Information Retrieval, 2009.
  18. Tao Qin, Tie-Yan Liu, Jun Xu, and Hang Li, LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval, Information Retrieval, 2009
  19. Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng Wang, Tie-Yan Liu, and Hang Li. Query-level Loss Function for Information Retrieval. Information Processing and Management, 2007.
  20. Tao Qin, Xu-Dong Zhang, Tie-Yan Liu, De-Sheng Wang, Hong-Jiang Zhang. An Active Feedback Framework for Image Retrieval, Pattern Recognition Letters, 2007.
  21. Ying Bao, Guang Feng, Tie-Yan Liu, Zhiming Ma and Ying Wang. Ranking Websites: A Probabilistic View, Internet Mathematics, 2007.
  22. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Guang Geng, De-Sheng Wang, and Wei-Ying Ma. Topic Distillation Via Subsite Retrieval, Information Processing and Management, 2006.
  23. Bin Gao, Tie-Yan Liu, Xin Zheng, Qiansheng Cheng, and Wei-Ying Ma. Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Co-partitioning, IEEE Transactions on Knowledge and Data Engineering, 2005.
  24. Tie-Yan Liu, Yiming Yang, Hao Wan, Hua-Jun Zeng, Zheng Chen, and Wei-Ying Ma. Support Vector Machines Classification with Very Large Scale Taxonomy, SIGKDD Explorations, 2005.
  25. Tie-Yan Liu, Kwoktung Lo, Xu-Dong Zhang, and Jian Feng. A New Cut Detection Algorithm with Constant False-Alarm Ratio for Video Segmentation, Journal of Visual Communications and Image Representation, 2004. 
  26. Tie-Yan Liu, Xu-Dong Zhang, Jian Feng, and Kwoktung Lo. Shot Reconstruction Degree: a Novel Criterion for Key Frame Selection, Pattern Recognition Letters, 2004.
  27. Tie-Yan Liu, Kwoktung Lo, Xu-Dong Zhang, and Jian Feng. Frame Interpolation Scheme Using Inertia Motion Prediction. Signal Processing: Image Communication, 2003.
  28. Tie-Yan Liu, Kwoktung Lo, Xu-Dong Zhang, and Jian Feng. Inertia-based Cut Detection and Its Integration with Video Coder. IEE Proceedings on Vision, Image and Signal Processing, 2003.

[Conference Papers]

  1. Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu and Tie-Yan Liu, Budgeted Multi-armed Bandits with Multiple Plays, IJCAI 2016.
  2. Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang and Tie-Yan Liu, Asynchronous Accelerated Stochastic Gradient Descent, IJCAI 2016.
  3. Yingce Xia, Tao Qin, Tie-Yan Liu, Best Action Selection in a Stochastic Environment, AAMAS 2016.
  4. Tie-Yan Liu, Weidong Ma, Pingzhong Tang, Tao Qin, Guang Yang, Bo Zheng, Online Non-Preemptive Story Scheduling in Web Advertising, AAMAS 2016
  5. Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu, Optimal Sample Size for Adword Auctions, AAMAS 2016.
  6. Shizhao Sun, Wei Chen, Liwei Wang, and Tie-Yan Liu, On the Depth of Deep Neural Networks: A Theoretical View, AAAI 2016.
  7. Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Thompson Sampling for Budgeted Multi-armed Bandits, IJCAI 2015.
  8. Bolei Xu, Tao Qin, Guoping Qiu, and Tie-Yan Liu, Competitive Pricing for Cloud Computing in an Evolutionary Market, IJCAI 2015.
  9. Changjun Wang, Weidong Ma, Tao Qin, Xujin Chen, Xiaodong Hu, and Tie-Yan Liu, Selling Reserved Instances in Cloud Computing, IJCAI 2015.
  10. Shanshan Huang, Shuaiqiang Wang, Tie-Yan Liu, Jun Ma, Zhumin Chen, and Jari Veijalainen, Listwise Collaborative Filtering, SIGIR 2015.
  11. Binyi Chen, Tao Qin, and Tie-Yan Liu, Mechanism Design for Daily Deals, AAMAS 2015.
  12. Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric Xing, Tie-Yan Liu, and Wei-Ying Ma, LightLDA: Big Topic Models on Modest Computer Cluster, WWW 2015.
  13. Tie-Yan Liu, Wei Chen, and Tao Qin, Mechanism Learning with Mechanism Induced Data, Senior Member Track, AAAI 2015.
  14. Haifang Li, Wei Chen, Fei Tian, Tao Qin, and Tie-Yan Liu, Generalization Analysis for Game-theoretic Machine Learning, AAAI 2015.
  15. Chang Xu, Yalong Bai, Jiang Bian, Bin Gao, and Tie-Yan Liu, A General Approach to Incorporate Knowledge into Word Representation, CIKM 2014.
  16. Fei Tian, Jiang Bian, Bin Gao, Hanjun Dai, Rui Zhang, and Tie-Yan Liu, A Scalable Probabilistic Model for Learning Multi-Prototype Word Embedding, COLING 2014.
  17. Siyu Qiu, Qing Cui, Jiang Bian, Bin Gao, and Tie-Yan Liu, Co-learning of Word Representations and Morpheme Representations, COLING 2014.
  18. Bin Gao, Jiang Bian, and Tie-Yan Liu, Knowledge Powered Deep Learning for Word Embedding, ECML/PKDD 2014.
  19. Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang, Generalized Second Price Auction with Probabilistic Broad Match, EC 2014.
  20. Yingce Xia, Tao Qin and Tie-Yan Liu, Incentivizing High-quality Content from Heterogeneous Users: On the Existence of Nash Equilibrium, AAAI 2014.
  21. Fei Tian, Haifang Li, Wei Chen, Tao Qin and Tie-Yan Liu, Agent Behavior Prediction and Its Generalization Analysis, AAAI 2014.
  22. Fei Tian, Bin Gao and Tie-Yan Liu, Learning Deep Representations for Graph Clustering, AAAI 2014.
  23. Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang and Tie-Yan Liu, Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks, AAAI 2014.
  24. Tie-Yan Liu, Weidong Ma, Tao Qin, and Tao Wu, Generalized Second Price Auctions with Value Externalities, AAMAS 2014.
  25. Jiang Bian, Taifeng Wang, and Tie-Yan Liu, Sampling Dilemma: Towards Effective Data Sampling for Click Prediction in Sponsored Search, WSDM 2014.
  26. Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu, A Theoretical Analysis of NDCG Type Ranking Measures, COLT 2013.
  27. Weihao Kong, Jian Li, Tie-Yan Liu and Tao Qin, Optimal Allocation for Chunked-Reward Advertising, WINE 2013.
  28. Min Xu, Tao Qin, and Tie-Yan Liu, Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising, NIPS 2013.
  29. Taifeng Wang, Jiang Bian, Shusen Liu, Yuyu Zhang, and Tie-Yan Liu, Psychological Advertising: Exploring Consumer Psychology for Click Prediction in Sponsored Search, KDD 2013.
  30. Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu, A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search, IJCAI 2013.
  31. Wenkui Ding, Tao Qin, and Tie-Yan Liu, Multi-Armed Bandit with Budget Constraint and Variable Costs, AAAI 2013.
  32. Haifeng Xu, Diyi Yang, Bin Gao and Tie-Yan Liu, Predicting Advertiser Bidding Behaviors in Sponsored Search by Rationality Modeling, WWW 2013.
  33. Lei Yao, Wei Chen and Tie-Yan Liu, Convergence Analysis for Weighted Joint Strategy Fictitious Play in Generalized Second Price Auction, WINE 2012.
  34. Weinan Zhang, Ying Zhang, Bin Gao, Yong Yu, Xiaojie Yuan, and Tie-Yan Liu, Joint optimization of bid and budget allocation in sponsored search, KDD 2012.
  35. Chenyan Xiong, Taifeng Wang, Wenkui Ding, Yidong Shen, Tie-Yan Liu. Relational Click Prediction for Sponsored Search, WSDM 2012.
  36. Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-Yan Liu, Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space. NIPS 2012.
  37. Bin Gao, Tie-Yan Liu, Taifeng Wang, Wei Wei, and Hang Li, Semi-supervised graph ranking with rich meta data, KDD 2011
  38. Zhicong Cheng, Bin Gao, Congkai Sun, Yanbing Jiang, and Tie-Yan Liu. Let Web Spammers Expose Themselves, WSDM 2011.
  39. Zhicong Cheng, Bin Gao, and Tie-Yan Liu, Actively Predicting Diverse Search Intent from User Browsing Behaviors, WWW 2010.
  40. Tao Qin, Xiubo Geng, and Tie-Yan Liu, A New Probabilistic Model for Rank Aggregation, NIPS 2010.
  41. Wei Chen, Tie-Yan Liu, Zhiming Ma, Two-Layer Generalization Analysis for Ranking Using Rademacher Average, NIPS 2010.
  42. Jiang Bian, Tie-Yan Liu, Tao Qin, and Hongyuan Zha, Query-dependent Loss Function for Web Search. WSDM 2010.
  43. Fen Xia, Tie-Yan Liu, Hang Li, Statistical Consistency of Top-k Ranking, NIPS 2009.
  44. Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, Hang Li, Ranking Measures and Loss Functions in Learning to Rank, NIPS 2009.
  45. Yanyan Lan, Tie-Yan Liu, Zhiming Ma, and Hang Li. Generalization Analysis for Listwise Learning to Rank Algorithms, ICML 2009.
  46. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. Global Ranking Using Continuous Conditional Random Fields, NIPS 2008.
  47. Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. Listwise Approach to Learning to Rank: Theory and Algorithm, ICML 2008.
  48. Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, and Hang Li. Query-level Stability and Generalization in Learning to Rank, ICML 2008.
  49. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Wen-Ying Xiong, and Hang Li. Learning to Rank Relational Objects and Its Application to Web Search, WWW 2008.
  50. Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li, and Heung-Yeung Shum. Query-dependent Ranking using K-Nearest Neighbor, SIGIR 2008.
  51. Yuting Liu, Bin Gao, Tie-Yan Liu, Ying Zhang, Zhiming Ma, Shuyuan He, and Hang Li. BrowseRank: Letting Web Users Vote for Page Importance, SIGIR 2008.
  52. Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, and Wei-Ying Ma. Directly Optimizing IR Evaluation Measures in Learning to Rank, SIGIR 2008.
  53. Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. Learning to Rank: From Pairwise Approach to Listwise Approach. ICML 2007.
  54. Yuting Liu, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, and Hang Li. Supervised Rank Aggregation, WWW 2007.
  55. Xiubo Geng, Tie-Yan Liu, Tao Qin, and Hang Li. Feature Selection for Ranking, SIGIR 2007.
  56. Mingfeng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: A Ranking Method with Fidelity Loss, SIGIR 2007.
  57. Tao Qin, Tie-Yan Liu, Wei Lai, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. Ranking with Multiple Hyperplanes, SIGIR 2007.
  58. Guang Feng, Tie-Yan Liu, Ying Wang, Ying Bao, Zhiming Ma, Xu-Dong Zhang, and Wei-Ying Ma. AggregateRank: Bringing Order to Websites, SIGIR 2006.
  59. Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Huang, and Hsiao-Wuen Hon. Adapting Ranking SVM to Document Retrieval, SIGIR 2006.
  60. Qiankun Zhao, Chuhong Hoi, Tie-Yan Liu, Sourav S. Bhowmick, Michael R. Lyu, and Wei-Ying Ma. Time-Dependent Semantic Similarity Measure of Queries Using Historical Click-Through Data, WWW 2006.
  61. Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, and Wei-Ying Ma. Event Detection from Evolution of Click-through Data, KDD 2006.
  62. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Zheng Chen, and Wei-Ying Ma. A Study on Relevance Propagation for Web Search, SIGIR 2005.
  63. Bin Gao, Tie-Yan Liu, Xin Zheng, Qian-Sheng Cheng, and Wei-Ying Ma. Consistent Bipartite Graph Co-Partitioning for Star-Structured High-Order Heterogeneous Data Co-Clustering, KDD 2005.
  64. Bin Gao, Tie-Yan Liu, Tao Qin, Xin Zheng, Qian-Sheng Cheng, and Wei-Ying Ma. Web Image Clustering by Consistent Utilization of Visual Features and Surrounding Texts, ACM Multimedia 2005.
  65. Tie-Yan Liu, Tao Qin and Hong-Jiang Zhang. Time-constraint Boost for TV Commercials Detection. IEEE ICIP 2004.
  66. Bin Gao, Tie-Yan Liu, Qian-Sheng Cheng, and Wei-Ying Ma. A Linear Approximation Based Method for Noise-Robust and Illumination-invariant Image Change Detection. PCM 2004.
  67. Tie-Yan Liu, Kwok-Tung Lo, Xu-Dong Zhang, and Jian Feng. Constant False-alarm Ratio Processing for Video Cut Detection, IEEE ICIP 2002.
  68. Jian Feng, Tie-Yan Liu, Kwok-Tung Lo , and Xu-Dong Zhang. Adaptive Motion Tracking for Fast Block Motion Estimation, IEEE ISCAS 2001.

Professional Activities

  • General Co-Chair, ICTIR 2018.
  • PC Co-Chair, SocInfo 2015, ACML 2015, WINE 2014, AIRS 2013, RIAO 2010.
  • Tutorial Co-Chair, SIGIR 2016, WWW 2014
  • Doctorial Consortium Co-Chair, WSDM 2015.
  • PhD Symposium Co-Chair, WWW 2016.
  • Local Co-Chair, ICML 2014.
  • Demo/exhibition Co-Chair, KDD 2012.
  • Track Chair / Area Chair/ Senior PC member, IJCAI 2016, KDD 2016, AAAI 2016, NIPS 2015, IJCAI 2015, KDD 2015, WWW 2015, ACML 2014, IJCAI 2013, WWW 2011. SIGIR 2008-2011, AIRS 2009-2011.
  • Associate Editor, ACM Transactions on Information System, ACM Transactions on Web.
  • Editorial Board Member, Information Retrieval Journal, and Foundations and Trends in Information Retrieval.
  • Guest Editor, Special issue on Machine Learning in Asia, Machine Learning Journal 2016, Special issue on Learning to Rank for IR, Information Retrieval Journal 2010; Special issue on Learning to Rank Challenge, Journal of Machine Learning Research 2011.
  • Tutorial speaker, KDD 2012, SIGIR 2012, WWW 2011, SIGIR 2010, WWW 2009, WWW 2008, SIGIR 2008.
  • Keynote speaker, ACML 2016, ECML/PKDD 2014, ORSC 2014, CCIR 2014, CCML 2013, PCM 2010, CCIR 2011.
  • Plenary Panelist, KDD 2011.
  • Workshop Co-chair, KDD Workshop on Internet Economics and Online Advertising (ADKDD), 2012; SIGIR Workshop on Online Advertising, 2011; NIPS Workshop on Machine Learning in Online Advertising, 2010; ICML Workshop on Learning to Rank, 2010; SIGIR Workshop on Learning to Rank, 2007-2009.
  • Regularly serve as program committee member / reviewer for many leading international conferences, including SIGIR, NIPS, ICML, KDD, AAAI, WWW, WSDM, SDM, ICDM, CIKM, ECIR, ACL, ICIP, etc.
  • Senior member of IEEE, ACM, and CCF.
  • Distinguished speaker of CCF.
  • Academic committee member, CCF.
  • Co-chair, MSRA fellowship program.
  • Co-director, MOE MSRA-HKUST joint lab.
  • Adjunct professor of the Carnegie Mellon University (LTI), Nankai University, University of Science and Technology of China, and Sun Yat-Sen University
  • Honorary professor of University of Nottingham.

Awards and Honors

  • Top-10 computer science books by Chinese authors (2015), Springer – Learning to Rank for Information Retrieval
  • Research Break-through Award (2012), Microsoft Research Asia – Computational Economics
  • Best Student Paper Award (2008), ACM SIGIR – BrowseRank: Letting Web Users Vote for Page Importance
  • Most Cited Paper Award (2004-2006), Journal of Visual Communication and Image Representation (JVCIR) – A New Cut Detection Algorithm with Constant False-Alarm Ratio for Video Segmentation
  • Ship-It awards, Technology Transfer Awards, Gold Star Award, and 100+ Patent awards, Microsoft

Dataset and Toolkit Release

  • Microsoft Distributed Machine Learning Toolkit (http://www.dmtk.io/), 2015 – Attracted millions of page views, hundreds of thousands of downloads, and thousands of stars at GitHub
  • LETOR Benchmark Dataset for Learning to Rank (http://research.microsoft.com/en-us/um/beijing/projects/letor/), 2007 – A must-have experimental platform for research on learning to rank. According to incomplete statistics, more than half of the papers on learning to rank published at major conferences and journals have used this dataset for their evaluations in the past ten years.

We Are Hiring!

We are hiring at all levels (from fresh graduates to experienced researchers)! If your major is machine learning, information retrieval, or algorithmic game theory, and you have the passion to change the world, please send your resume to tyliu@microsoft.com.


Microsoft Learning to Rank Datasets

Established: June 10, 2010

We released two large scale datasets for research on learning to rank: MSLR-WEB30k with more than 30,000 queries and a random sampling of it MSLR-WEB10K with 10,000 queries.   Dataset Descriptions The datasets are machine learning data, in which queries…