In his 1889 poem “Ballad of East and West” Rudyard Kipling wrote:
Oh, East is East, and West is West, and never the twain shall meet,
Till Earth and Sky stand presently at God’s great Judgment Seat;
This panel will relive Kipling’s ballad with academia and industry playing the role of the east and west. As in Kipling’s poem, it is indisputable that academia and industry can, at times be, opposite sides of the spectrum. They move at different paces, have different focus, and different objectives. If only we had a penny for each time industry complain about how “academia obsesses about wrong problems” or “does not prepare students for the real world“, or for academics who complain about industry being “too short-sighted” or “make choices that are more expedient than correct“, or “are rediscovering the wheel”! After all, it was only in 1990s that industry considered transactions dead and papers on it not worthy of being published. Ten years later, the emergence of the cloud, big data centers, and Eric Brewer’s CAP theorem, changed all that. One can create similar examples for a variety of technologies — XML, graphs, query processing, map reduce to name a few.
So here is a chance for academia and industry to tell each other as it is: what topics are oversold and over-researched today? What would the industry want academia not to focus on and why? what would they wish for academia to work on instead? Likewise, do academics still consider industrial research to be shortsighted? Which big opportunities are they missing, and how can they change that?….
The next two lines of Kipling’s Ballad go onto state:
But there is neither East nor West, Border, nor Breed, nor Birth,
When two strong men stand face to face, though they come from the ends of the earth!
So after spending half the panel “telling” the “other” what they are doing wrong, the panel will shift its focus to bringing the two strong “entities” (replacing Kipling’s mention of “men” by “entities” seems apt not only since we seek gender equality, but also given the database context) to explore what “relationships”, interaction, and conversations could lead to convergence of thoughts and ideas to help create bring the two together in creating a vibrant database research community.
Finding the individuals to play the role of the two (in our case 6) protagonists – that represent the Colonel’s son and Kamal was the easy part. We have a dream team representing thought leaders in academia and industry. The hard part is to agree on what constitutes the “Colonel’s mare” that Kamal stole, and the “Son’s pistol” that he gave as a gift in exchange.
Join us to find out!
Philip A. Bernstein is a Distinguished Scientist at Microsoft Research. He has published over 150 papers and two books on the theory and implementation of database systems, especially on transaction processing and data integration, which are still a major focus of his research. He is an ACM Fellow, a AAAS Fellow, a winner of ACM SIGMOD’s Codd Innovations Award, a member of the Washington State Academy of Sciences, and the U.S. National Academy of Engineering. He received a B.S. degree from Cornell and M.Sc. and Ph.D. from University of Toronto.
Dr. Laura Haas joined the University of Massachusetts Amherst in August 2017 as Dean of the College of Information and Computer Sciences, after a long career at IBM, where she was accorded the title IBM Fellow in recognition of her impact. At the time of her retirement from IBM, she was Director of IBM Research’s Accelerated Discovery Lab (2011-2017), after serving as Director of Computer Science at IBM’s Almaden Research Center from 2005 to 2011. She had worldwide responsibility for IBM Research’s exploratory science program from 2009 through 2013. From 2001-2005, she led the Information Integration Solutions architecture and development teams in IBM’s Software Group. Previously, Dr. Haas was a research staff member and manager at Almaden. She is best known for her work on the Starburst query processor, from which DB2 LUW was developed, on Garlic, a system which allowed integration of heterogeneous data sources, and on Clio, the first semi-automatic tool for heterogeneous schema mapping. She has received several IBM awards for Outstanding Innovation and Technical Achievement, an IBM Corporate Award for information integration technology, the Anita Borg Institute Technical Leadership Award, the IEEE Computer Society Computer Pioneer Award, and the ACM SIGMOD Edgar F. Codd Innovation Award. Dr. Haas was Vice President of the VLDB Endowment Board of Trustees from 2004-2009 and served on the board of the Computing Research Association from 2007-2016 (vice chair 2009-2015); she currently serves as Chair of the National Academies Computer Science and Telecommunications Board. She is an ACM Fellow, a member of the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences.
FeiFei Li is currently a Vice President of Alibaba Group, ACM Distinguished Scientist, President of the Database Products Business Unit of Alibaba Cloud Intelligence, and Director of the Database and Storage Lab of DAMO academy. He has won multiple awards from ACM and IEEE and others. He is a recipient of the IEEE ICDCS best paper award, ACM SoCC 2019 Best Paper Award Runner-up, IEEE ICDE 2014 10 Years Most Influential Paper Award, ACM SIGMOD 2016 Best Paper Award, ACM SIGMOD 2015 Best System Demonstration Award, IEEE ICDE 2004 Best Paper Award. He has been an associate editor, PC co-chairs, and core committee members for many prestigious journals and conferences, and has led the R&D efforts of building cloud native database systems and products at Alibaba.
H. V. Jagadish (Jag) is a computer scientist in the field of database systems research. He is a Fellow of ACM, Fellow of AAAS, the Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan at Ann Arbor, the director of MIDAS (Michigan Institute for Data Science), and a Senior Scientific Director of the National Center for Integrative Biomedical Informatics established by the National Institutes of Health. Prior to joining the Michigan faculty, he spent over a decade at AT&T Bell Laboratories as a research scientist where he would eventually become head of the Database division Jagadish earned his bachelor’s degree from the Indian Institute of Technology, Delhi and a doctorate in Electrical Engineering from Stanford University in 1985. He was elected Fellow of the Association for Computing Machinery in 2003 and trustee of the VLDB Endowment in 2004. He was the founding editor of the Proceedings of the VLDB Endowment (PVLDB) in 2008. He was recognized with a Contributions Award by the ACM SIGMOD in 2013. In 2017, he was elected as Fellow of American Association for the Advancement of Science. Jagadish sits on the Computer Science Advisory Board of University of the People.
Volker Markl is a Full Professor and Chair of the Database Systems and Information Management (DIMA) Group at the Technische Universität Berlin (TU Berlin). At the German Research Center for Artificial Intelligence (DFKI), he is Chief Scientist and Head of the Intelligent Analytics for Massive Data Research Group. In addition, he is Director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), a merger of the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). BIFOLD is one of Germany’s national Competence Centers for Artificial Intelligence and will further bolster ongoing collaborative research in scalable data management and Machine Learning. Dr. Markl is a database systems researcher conducting research at the intersection of distributed systems, scalable data processing, text mining, computer networks, machine learning, and applications in healthcare, logistics, Industry 4.0, and information marketplaces. Earlier in his career, he was a Research Staff Member and Project Leader at the IBM Almaden Research Center in San Jose, California, USA and a Research Group Leader at FORWISS, the Bavarian Research Center for Knowledge-based Systems located in Munich, Germany. Volker Markl is a computer science graduate from Technische Universität München, where he earned his Diploma in 1995 with a thesis on exception handling in programming languages. He earned his PhD in 1999 the area of multidimensional indexing under the supervision of Rudolf Bayer.Volker Markl has published numerous scholarly papers on indexing, query optimization, lightweight information integration, and scalable data processing at prestigious venues. He holds 18 patents, has transferred technology into several commercial products, and has been involved in two successful startup exits. He has been both the Speaker and Principal Investigator for the Stratosphere Project, which resulted in a Humboldt Innovation Award as well as Apache Flink, the open-source big data analytics system. He currently serves as the President of the VLDB Endowment and was elected as one of Germany’s leading Digital Minds (Digitale Köpfe) by the German Informatics (GI) Society. Volker also is a member of the Scientific Advisory Board of Software AG. Most recently, Volker and his team earned the ACM SIGMOD 2020 Best Paper Award, for their work on „ Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects “.
Dr. Bhavani Thuraisingham is the Founders Chair Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute at the University of Texas at Dallas (UTD). She is also a visiting Senior Research Fellow at Kings College, University of London and an elected Fellow of the ACM, IEEE, the AAAS, the NAI and the BCS. She was a Cyber Security Policy Fellow at the New America Foundation in 2017-8. Her research interests are on integrating cyber security and artificial intelligence/data science for the past 35 years (it used to be computer security and data management/mining/expert systems). She has received several awards including the IEEE CS 1997 Technical Achievement Award, ACM SIGSAC 2010 Outstanding Contributions Award, the IEEE Comsoc Communications and Information Security 2019 Technical Recognition Award, the IEEE CS Services Computing 2017 Research Innovation Award, the ACM CODASPY 2017 Lasting Research Award, the IEEE ISI 2010 Research Leadership Award, the 2017 Dallas Business Journal Women in Technology Award, and the ACM SACMAT 10 Year Test of Time Awards for 2018 and 2019 (for papers published in 2008 and 2009). She co-chaired the Women in Cyber Security Conference (WiCyS) in 2016 and delivered the featured address at the 2018 Women in Data Science (WiDS) at Stanford University as well as keynote addresses at Cyber-W 2017 and 2020 (Women in Cyber Security Research), 2019 Women in Communications Engineering (WICE), and 2018 Women in Services Computing, and serves as the Co-Director of both the Women in Cyber Security and Women in Data Science Centers at UTD. Her 40-year career includes industry (Control Data, Honeywell), federal research laboratory (MITRE), US government (NSF) and US Academia. Her work has resulted in 130+ journal articles, 300+ conference papers, 150+ keynote and featured addresses, six US patents, fifteen books as well as technology transfer of the research to commercial products and operational systems. She received her PhD from the University of Wales, Swansea, UK, and the prestigious earned higher doctorate (D. Eng) from the University of Bristol, UK.
Lei Chen, is a chair professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST), Fellow of the IEEE, and a Distinguished Member of the ACM. Currently, Prof. Chen serves as the director of Big Data Institute at HKUST, director of Master of Science on Big Data Technology and director of HKUST MOE/MSRA Information Technology Key Laboratory. Prof. Chen’s research interests include Data-driven AI, knowledge graphs, blockchains, data privacy, crowdsourcing, spatial and temporal databases and query optimization on large graphs and probabilistic databases. He received his BS degree in computer science and engineering from Tianjin University, Tianjin, China, MA degree from Asian Institute of Technology, Bangkok, Thailand, and PhD in computer science from the University of Waterloo, Canada. Prof. Chen received the SIGMOD Test-of-Time Award in 2015.The system developed by Prof. Chen’s team won the excellent demonstration award in VLDB 2014. Prof. Chen had served as VLDB 2019 PC Co-chair. Currently, Prof. Chen serves as Co-Editor-in-Chief of VLDB Journal and associate editor-in-chief of IEEE Transaction on Data and Knowledge Engineering.
Sharad Mehrotra, is a Professor of Computer Science at the University of California, Irvine, Fellow of the IEEE, and a Distinguished Member of the ACM. His primary research interests include scalable data analytics, data cleaning, big data, distributed systems, secure databases, privacy, and Internet of Things. He currently leads a DARPA funded TIPPERS project that is building an IoT data management middleware that supports plug-n-play support for diverse privacy technologies and for scalable analytics on such data. He received his B.Tech at IIT Kanpur, India and an MS and PhD degree in Computer Science at the University of Texas, Austin, in 1993. He served as a Scientist at Matsushita Information Technology Laboratory from 1993-94 where he designed a Concurrent Text Indexing Engine. He was an Assistant Professor at the University of Illinois at Urbana Champaign from 1994-98 where he led the development of a database system, entitled MARS, that provided native support for content-based retrieval from images in databases. At UCI he has led several large multidisciplinary projects including Responding to the Unexpected and Crisis Events (RESCUE) funded through the NSF large-ITR award . He has received over 10 best paper awards including 2011 SIGMOD Best Paper Award, 2012 SIGMOD Test of Time award, DASFAA ten year best paper awards for 2013 and 2014, ACM ICMR best paper award for 2013, IEEE NCA Best paper award for 2019, IEEE SmartComp 2021, a Dean’s Award for Research in 2016, and a CAREER Award in 1998 from the US National Science Foundation (NSF).