刘铁岩（研究员）的个人简介
刘铁岩博士毕业于清华大学电子工程系。现任微软亚洲研究院主任研究员，互联网经济与计算广告学研究组负责人。他是美国计算机学会(ACM)、国际电子电气工程师学会(IEEE)、和中国计算机学会（CCF）的高级会员。中国科技大学和南开大学的客座教授。
个人资料
刘铁岩博士是机器学习和信息检索领域的知名专家，尤其在排序学习方面取得了国际领先的研究成果。他著有《排序学习及其在信息检索中的应用》等学术专著。他在国际顶级期刊和会议上发表相关论文70余篇。他持有40余项美国和国际专利。他的论文曾获得国际信息检索大会(SIGIR)最佳学生论文奖，和国际期刊《视觉通信和图像表示》的最高引用论文奖。
他是国际计算机辅助搜索会议(RIAO) 2010年度的程序委员会主席，国际信息检索大会(SIGIR)2008-2011年度的领域主席(Area Chair)，亚洲信息检索会议(AIRS) 2009-2011年度的领域主席，国际数据挖掘大会（KDD）2012年度的展览和演示主席，国际互联网大会（WWW）2011年度的领域主席。他担任美国计算机学会会刊《信息系统（TOIS）》的副主编，国际期刊《信息检索》和《人工智能》的编委，和数十个国际期刊的审稿专家。他是包括WWW, SIGIR, ICML, ACL, ICIP等在内的三十几个国际会议的程序委员会成员(Program Committee Member)，是国际排序学习研讨会(LR4IR)2007-2009年度的联合主席(Co-chair)，和2010年排序学习竞赛的联合组织者。他曾经在WWW、SIGIR、KDD等国际会议上做关于排序学习的主题讲座(tutorial)，并受邀作为KDD 2011年度的大会主题辩论嘉宾(panelist)。他受邀为亚太多媒体大会（PCM 2010）和中国信息检索大会（CCIR 2011）做大会特邀报告（keynote）。他还受邀为包括卡耐基梅隆大学（CMU）在内的十余所国内外高校讲授《排序学习》和《机器学习》的课程。
代表作
Internet Economics
● Joint Optimization of Bid and Budget Allocation in Sponsored Search,KDD2012
● Relational Click Prediction for Sponsored Search,WSDM2012.
● An Advertiser-Centric Approach to Understand User Click Behavior in Sponsored Search,CIKM2011.
Learning to Rank
● Learning to Rank for Information Retrieval,Foundation and Trends on Information Retrieval, Now Publishers, 2009.
● A Noise-Tolerant Graphical Model for Ranking,Information Processing and Management, 2011.
● Future research directions on learning to rank, Proceeding track,Journal of Machine Learning Research, 2011.
● Selecting Optimal Training Data for Learning to Rank,Information Processing and Management, 2011.
● A New Probabilistic Model for Rank Aggregation,NIPS 2010.
● Two-Layer Generalization Analysis for Ranking Using Rademacher Average,NIPS 2010.
● Statistical Consistency of Top-k Ranking,NIPS 2009.
● Ranking Measures and Loss Functions in Learning to Rank,NIPS 2009.
● Global Ranking Using Continuous Conditional Random Fields,NIPS 2008.
● Generalization Analysis of Listwise Learning to Rank Algorithms,ICML 2009.
● Listwise Approach to Learning to Rank: Theorem and Algorithm,ICML 2008.
● Query-level Stability and Generalization in Learning to Rank,ICML 2008.
● Learning to Rank: From Pairwise Approach to Listwise Approach.ICML 2007.
● Query-dependent Ranking using K-Nearest Neighbor,SIGIR 2008.
● Directly Optimizing IR Evaluation Measures in Learning to Rank,SIGIR 2008.
● Making LETOR More Useful and Reliable,LR4IR 2008,in conjunction withSIGIR 2008.
● Feature Selection for Ranking,SIGIR 2007.
● FRank:A Ranking Method with Fidelity Loss,SIGIR 2007.
● Ranking with Multiple Hyperplanes,SIGIR 2007.
● LETOR: Benchmark dataset for research on learning to rank for information retrieval,LR4IR 2007, in conjunction withSIGIR 2007.
● Adapting Ranking SVM to Document Retrieval,SIGIR 2006.
● Learning to Rank Relational Objects and Its Application to Web Search,WWW 2008.
● Supervised Rank Aggregation,WWW 2007.
● Ranking with query-dependent loss for web search.WSDM 2010
● Tendency Correlation Analysis for Direct Optimization of Evaluation Measures in Information Retrieval,Information Retrieval Journal, 2010.
● Introduction to special issue on learning to rank for information retrieval,Information Retrieval Journal, 2010.
● A General Approximation Framework for Direct Optimization of Information Retrieval Measures,Information Retrieval Journal, 2009.
Web Search
● Semi-supervised graph ranking with rich meta data,KDD 2011.
● Page Importance Computation based on Markov Processes,Information Retrieval, 2011
● Let Web Spammers Expose Themselves,WSDM 2011.
● Actively Predicting Diverse Search Intent from User Browsing Behaviors,WWW 2010.
● A Framework to Compute Page Importance based on User Behaviors,Information Retrieval Journal, 2009.
● BrowseRank: Letting Web Users Vote for Page Importance,SIGIR 2008.[SIGIR Best Student Paper Award]
● AggregateRank: Bringing Order to Websites,SIGIR 2006.
● A Study on Relevance Propagation for Web Search,SIGIR 2005.
● Time-Dependent Semantic Similarity Measure of Queries Using Historical Click-Through Data,WWW 2006.
● Event Detection from Evolution of Click-through Data,KDD 2006.
● Consistent Bipartite Graph Co-Partitioning for Star-Structured High-Order Heterogeneous Data Co-Clustering,KDD 2005.
● Ranking Websites: A Probabilistic View,Internet Mathematics, 2007.
● Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Co-partitioning,IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2005.
● Support Vector Machines Classification with Very Large Scale Taxonomy,SIGKDD Explorations, 2005.
Multimedia
● A New Cut Detection Algorithm with Constant False-Alarm Ratio for Video Segmentation,Journal of Visual Communications and Image Representation, 2004.[Most Cited Paper Award]
● Shot Reconstruction Degree: a Novel Criterion for Key Frame Selection,Pattern Recognition Letters, 2004.
● Frame Interpolation Scheme Using Inertia Motion Prediction.Signal Processing: Image Communication, 2003.
● Inertia-based Cut Detection and Its Integration with Video Coder.IEE Proceedings on Vision, Image and Signal Processing, 2003.
