Big data, data science, AI and deep learning are the latest craze in industry and academia, with huge numbers of dollars being devoted to related applications, including face recognition, machine translation, digital assistants, self driving cars, ad-serving, chat-bots, and more. Although these emerging applications rely on massive amounts of evolving data that needs to be cleaned, integrated, and analyzed for modeling purposes, data management issues are not usually perceived as central to these applications. In this panel, we will explore the key data management challenges and opportunities for data management in this new world, and discuss how the database community can best contribute to these exciting domains.
Ihab F. Ilyas
Professor, Cheriton School of Computer Science, University of Waterloo
Ihab Ilyas is a professor in the Cheriton School of Computer Science at the University of Waterloo. He received his PhD in computer science from Purdue University, West Lafayette. His main research is in the area of database systems, with special interest in data quality, data integration, managing uncertain data, rank-aware query processing, and information extraction. Ihab is a recipient of the Ontario Early Researcher Award (2009), a Cheriton Faculty Fellowship (2013), an NSERC Discovery Accelerator Award (2014), and a Google Faculty Award (2014). He is an ACM Distinguished Scientist, an elected member of the VLDB Endowment Board of Trustees, and an associate editor of the ACM Transactions of Database Systems (TODS). Ihab is a co-founder of Tamr, a startup focusing on large-scale data integration and cleaning.
H. V. Jagadish
University of Michigan
H. V. Jagadish is Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science, and Distinguished Scientist at the Institute for Data Science, at the University of Michigan in Ann Arbor. Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ.
Professor Jagadish is well known for his broad-ranging research on information management, and has approximately 200 major papers and 37 patents. He is a fellow of the ACM, "The First Society in Computing," (since 2003) and serves on the board of the Computing Research Association (since 2009). He has been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014). Since 2016, he is Editor of the Morgan & Claypool "Synthesis" Lecture Series on Data Management. Among his many awards, he won the ACM SIGMOD Contributions Award in 2013 and the David E Liddle Research Excellence Award (at the University of Michigan) in 2008.
Sihem Amer-Yahia is DR1 CNRS at LIG in Grenoble where she leads the SLIDE team. Her research interests are in social data management and crowdsourcing. She previously held research positions at QCRI, Yahoo! Research and AT&T Labs. Sihem served on the ACM SIGMOD Executive Board and the VLDB Endowment. She is Editor-in-Chief of the VLDB Journal and is on the editorial boards of TODS and Information Systems. She is currently chairing the VLDB Workshops 2016 and will chair VLDB 2018. Sihem completed her Ph.D. in Computer Science at INRIA in 1999, and her Diplôme d'Ingénieur at INI, Algeria.
University of Illinois (UIUC)
Aditya Parameswaran is an Assistant Professor in Computer Science at the University of Illinois. He spent a year as a PostDoc at MIT in 2013-14 following his PhD at Stanford University. He develops systems and algorithms for "human-in-the-loop" data analytics, synthesizing techniques from data mining, database systems, and human computation. He has received multiple dissertation awards (from SIGMOD, SIGKDD, and Stanford), an "Excellent" Lecturer award from Illinois, a Google Faculty award, the Key Scientific Challenges award from Yahoo!, four best-of-conference citations, and a Gold Medal from IIT Bombay. His research group is supported with funding from the Siebel Energy Institute, the NIH, the NSF, and Google.
Sunita Sarawagi researches in the fields of databases and machine learning. She is professor at IIT Bombay. She got her PhD in databases from the University of California at Berkeley and a bachelors degree from IIT Kharagpur. She has also worked at Google Research (2014-2016), CMU (2004), and IBM Almaden Research Center (1996-1999). She has several publications including best paper awards at ACM SIGMOD, VLDB, ICDM, NIPS, and ICML conferences. She has served on the board of directors of the ACM SIGKDD and VLDB foundation. She was program chair for the ACM SIGKDD 2008 conference, research track co-chair for the VLDB 2011 conference and has served as program committee member for SIGMOD, VLDB, SIGKDD, ICDE, and ICML conferences, and on the editorial boards of the ACM TODS and ACM TKDD journals.