CIKM Keynote Speakers

1.  Keynote, 10/25/2016 Tuesday, 9:20am-10:30am
Toward Data-Driven Education
Rakesh Agrawal (Data Insights Laboratories)
Room: Regency Ball Room
keynotes_0002_Rakesh-AgrawalAbstract: A program of study can be viewed as a knowledge graph consisting of learning units and relationships between them. Such a knowledge graph provides the core data structure for organizing and navigating learning experiences. We address three issues in this talk. First, how can we synthesize the knowledge graph, given a set of concepts to be covered in the study program. Next, how can we use data mining to identify and correct deficiencies in a knowledge graph. Finally, how can we use data mining to form study groups with the goal of maximizing overall learning. We conclude by pointing out some open research problems.
Bio: Rakesh Agrawal is the President and Founder of the Data Insights Laboratories. He is a member of the National Academy of Engineering, a Fellow of ACM, and a Fellow of IEEE. He has been both an IBM Fellow and a Microsoft Fellow. ACM SIGKDD awarded him its inaugural Innovations Award and ACM SIGMOD the Edgar F. Codd Award. He was named to the Scientific American’s First list of top 50 Scientists. Rakesh has been granted 80+ patents and published 200+ papers, including the 1st and 2nd highest cited in databases and data mining. Four of his papers have received “test-of-time” awards. His research formed the nucleus of IBM Intelligent Miner that led the creation of data mining as a new software category. Besides Intelligent Miner, several other commercial products incorporate his work, including IBM DB2 and WebSphere and Microsoft Bing.


2. Keynote, 10/26/2016 Wednesday, 9:00am-10:10am
Personalized Search: Potential and Pitfalls
Susan Dumais (Microsoft Research)
Room: Regency Ball Room
keynotes_0000_susanAbstract: Traditionally search engines have returned the same results to everyone who asks the same question. However, using a single ranking for everyone in every context at every point in time limits how well a search engine can do in providing relevant information. In this talk I present a framework to quantify the “potential for personalization” which we use to characterize the extent to which different people have different intents for the same query. I describe several examples of how we represent and use different kinds of contextual features to improve search quality for individuals and groups. Finally, I conclude by highlighting important challenges in developing personalized systems at Web scale including privacy, transparency, serendipity, and evaluation.
Bio: Susan Dumais a Distinguished Scientist and Deputy Managing Director of the Microsoft Research Lab in Redmond, and an adjunct professor at the University of Washington. Prior to joining Microsoft, she was at Bell Labs where she worked on Latent Semantic Analysis, techniques for combining search and browsing, and organizational impacts of new technology. Her current research focuses on user modeling and personalization, context and search, and temporal dynamics of information. She has worked closely with several Microsoft groups (Bing, Windows Desktop Search, SharePoint, and Office Online Help) on search-related innovations. Susan has published widely in the fields of information science, human-computer interaction and cognitive science, and holds several patents on novel retrieval algorithms and interfaces. She is Past-Chair of ACM’s Special Interest Group in Information Retrieval (SIGIR), and serves on editorial boards, technical program committees, and government panels. She was elected to the CHI Academy in 2005, an ACM Fellow in 2006, received the SIGIR Gerard Salton Award for Lifetime Achievement in 2009, was elected to the National Academy of Engineering (NAE) in 2011, received the ACM Athena Lecturer and Tony Kent Strix Awards in 2014, was elected to the American Academy of Arts and Sciences (AAAS) in 2015, and received the Lifetime Achievement Award from Indiana University Department of Psychological and Brain Science in 2016.


3. Keynote,  10/27/2016 Thursday, 9:00am-10:10am
A Personal Perspective and Retrospective on Web Search Technology
Andrei Broder (Google Research)
Room: Regency Ball Room
keynotes_0001_andreiAbstract: This talk is a review of some Web research and predictions that I co-authored over the last two decades: both what turned out gratifyingly right and what turned out embarrassingly wrong. Topics will include near-duplicates, the Web graph, query intent, inverted indices efficiency, and others. While this seems a completely idiosyncratic collection there are in fact concealed connections that offer good clues to the big question: what will happen next?
Bio: Broder is a Distinguished Scientist at Google where he leads a multidisciplinary research team located across three continents. From 2005 to 2012 he was a Fellow and VP for Computational Advertising at Yahoo. Previous positions include Distinguished Engineer at IBM and VP for Research and Chief Scientist at AltaVista. He was graduated Summa cum Laude from Technion and obtained his M.Sc. and Ph.D. in Computer Science at Stanford under Don Knuth. Broder has authored more than a hundred papers and was awarded fifty US patents. His current research interests are focused on user understanding, computational advertising, context-driven information supply, and randomized algorithms. He is a member of the US National Academy of Engineering and a Fellow of ACM and of IEEE. Other honors include the ACM Paris Kanellakis Theory and Practice Award and a doctorate Honoris Causa from Technion.