Invited Speakers

Speaker Details

Xin Luna Dong (Amazon)

Talk: Challenges and Innovations in Building a Product Knowledge Graph

Bio: Xin Luna Dong is a Principal Scientist at Amazon, leading the efforts of constructing Amazon Product Knowledge Graph. She was one of the major contributors to the Google Knowledge Vault project, and has led the Knowledge-based Trust project, which is called the “Google Truth Machine” by Washington’s Post. She has co-authored book “Big Data Integration”, published 70+ papers in top conferences and journals, and given 30+ keynotes/invited-talks/tutorials. She got the VLDB Early Career Research Contribution Award for advancing the state of the art of knowledge fusion, and got the Best Demo award in Sigmod 2005. She is the PC co-chair for Sigmod 2018 and WAIM 2015, and serves as an area chair for Sigmod 2017, CIKM 2017, Sigmod 2015, ICDE 2013, and CIKM 2011.

Oren Etzioni (Allen Institute for Artificial Intelligence)

Talk: Project Alexandria: Constructing a KB of Common Sense

Bio: Dr. Oren Etzioni is Chief Executive Officer of the Allen Institute for Artificial Intelligence. He has been a Professor at the University of Washington’s Computer Science department since 1991, receiving several awards including GeekWire’s Hire of the Year (2014), Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He was also the founder or co-founder of several companies including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013), and the author of over 100 technical papers that have garnered over 25,000 citations. The goal of Oren’s research is to solve fundamental problems in AI, particularly the automatic learning of knowledge from text. Oren received his Ph.D. from Carnegie Mellon University in 1991, and his B.A. from Harvard in 1986.

Tim Finin (University of Maryland, Baltimore County)

Talk: From Strings to Things: Populating Knowledge Bases from Text

Bio: Dr. Tim Finin is the Willard and Lillian Hackerman Chair in Engineering and a Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He has over 35 years of experience in applications of artificial intelligence to problems in information systems and language understanding. His current research is focused on the Semantic Web, mobile computing, analyzing and extracting information from text, and on enhancing security and privacy in information systems. He is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI), an IEEE technical achievement award recipient and was selected as the UMBC Presidential Research Professor in 2012.

Alon Halevy (Recruit Institute of Technology)

Talk: Creating Knowledge Bases from Text

Bio: Alon Halevy is the C.E.O of the Recruit Institute of Technology. From 2005 to 2015 he headed the Structured Data Management Research group at Google. Prior to that, he was a professor of Computer Science at the University of Washington in Seattle, where he founded the Database Group. In 1999, Dr. Halevy co-founded Nimble Technology, one of the first companies in the Enterprise Information Integration space, and in 2004, Dr. Halevy founded Transformic, a company that created search engines for the deep web, and was acquired by Google. Dr. Halevy is a Fellow of the Association for Computing Machinery, the author of the book “The Infinite Emotions of Coffee”, and co-author of the book “Principles of Data Integration”.

Monica Lam (Stanford University)

Talk: Keeping the Internet Open with an Open-Source Programmable Virtual Assistant

Bio: Monica S. Lam has been a Professor in the Computer Science Department at Stanford University since 1988, and the Faculty Director of the Stanford MobiSocial Computing Laboratory. She received her PhD in Computer Science from Carnegie Mellon University. Her current research interest is in creating open social computing platforms. She has worked in the areas of high-performance computing, computer architecture, compiler optimizations, security analysis, virtualization-based computer management, and mobile/social software architectures. She is a co-author of the “Dragon Book”. Together with her students, she founded MokaFive Inc. in 2005 and MobiSocial Inc. in 2012. Monica is an ACM Fellow.

Christopher Ré (Stanford University)

Talk: Data Programming in Snorkel

Bio: Christopher (Chris) Ré is an assistant professor in the Department of Computer Science at Stanford University in the InfoLab who is affiliated with the Statistical Machine Learning Group, Pervasive Parallelism Lab, and Stanford AI Lab. His work’s goal is to enable users and developers to build applications that more deeply understand and exploit data. His contributions span database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016. In addition, work from his group has been incorporated into major scientific and humanitarian efforts, including the IceCube neutrino detector, PaleoDeepDive and MEMEX in the fight against human trafficking, and into commercial products from major web and enterprise companies. He received a SIGMOD Dissertation Award in 2010, an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, the MacArthur Foundation Fellowship in 2015, and an Okawa Research Grant in 2016.

Xifeng Yan (University of California at Santa Barbara)

Talk: Democratize Data Science: NLI to Data

Bio: Xifeng Yan is a professor at the University of California, Santa Barbara. He holds the Venkatesh Narayanamurti Chair of Computer Science. He received his Ph.D. from the University of Illinois at Urbana-Champaign in 2006 and was a research staff member at the IBM T. J. Watson Research Center between 2006 and 2008. He has been working on modeling, managing, and mining graphs in knowledge graphs, information networks, computer systems, social media and bioinformatics. His works were extensively referenced, with over 14,000 citations per Google Scholar. He received NSF CAREER Award, IBM Invention Achievement Award, ACM-SIGMOD Dissertation Runner-Up Award, and IEEE ICDM 10-year Highest Impact Paper Award.

Luke Zettlemoyer (University of Washington and Allen Institute for Artificial Intelligence)

Talk: End-to-end Learning for Broad Coverage Semantics

Bio: Luke Zettlemoyer is an Associate Professor in the Allen School of Computer Science & Engineering at the University of Washington, and also leads the AllenNLP project at the Allen Institute for Artificial Intelligence. His research is in core semantics, where he works on data and algorithms for learning to recover and make use of representations of the meaning of natural language text. Honors include multiple best papers, a PECASE award, and being named an Allen Distinguished Investigator. Luke received his PhD from MIT and was a postdoctoral fellow at the University of Edinburgh.