2019 Distinguished Lecture Series

Meet world-renowned researchers at lectures hosted by Computing Science. These are open to students, researchers and those working in industry and education to share the latest leading-edge research. Admission is free of charge. Contact is James Delgrande (james_delgrande@sfu.ca).

2019 Distinguished Lecture Series speakers

October 24, 2019: Dan Suciu: Computer Science Professor at the University of Washington 


KEY, Big Data Presentation Studio (formerly known as IRMACS)
Applied Sciences Building, Room 10900
SFU Burnaby campus, 8888 University Drive

2019 Distinguished Lecture Speakers

THURSDAY, October 24, 2019
2:30-3:30 p.m.  KEY, Big Data Presentation Studio (formerly known as IRMACS)
Applied Sciences Building, Room 10900

Dan Suciu: Professor at the University of Washington

Title: Probabilistic Databases: A Dichotomy Theorem and Limitations of DPLL Algorithms


Probabilistic Databases (PDBs) extend traditional relational databases by annotating each record with a weight, or a probability. The query evaluation problem, "given a query (a First Order Logic Sentence), compute its probability", is an instance of the weighted model counting problem of Boolean formulas, and has applications to Markov Logic Networks and to other Statistical Relational Models. I will present in this talk two results. The first is a dichotomy theorem stating that, for each universally (or existentially) quantified sentence without negation, computing its probability is either #P-hard, or is in PTIME in the size of the probabilistic database. The second result is a limitation of Davis-Putnam-Logemann-Loveland (DPLL) algorithms: there exists FOL sentences that can be computed in PTIME over probabilistic databases (using lifted inference) yet every DPLL algorithm, even extended with caching and with components, takes exponential time.


Dan Suciu is a Professor in Computer Science at the University of Washington. He received his Ph.D. from the University of Pennsylvania in 1995, was a principal member of the technical staff at AT&T Labs and joined the University of Washington in 2000. Suciu is conducting research in data management, with an emphasis on topics related to Big Data and data sharing, such as probabilistic data, data pricing, parallel data processing, data security. He is a co-author of two books Data on the Web: from Relations to Semistructured Data and XML, 1999, and Probabilistic Databases, 2011. He is a Fellow of the ACM, holds twelve US patents, received the best paper award in SIGMOD 2000, SIGMOD 2019 and ICDT 2013, the ACM PODS Alberto Mendelzon Test of Time Award in 2010 and in 2012, the 10 Year Most Influential Paper Award in ICDE 2013, the VLDB Ten Year Best Paper Award in 2014, and is a recipient of the NSF Career Award and of an Alfred P. Sloan Fellowship. Suciu serves on the VLDB Board of Trustees, and is an associate editor for the Journal of the ACM, VLDB Journal, ACM TWEB, and Information Systems and is a past associate editor for ACM TODS and ACM TOIS. Suciu's PhD students Gerome Miklau, Christopher Re and Paris Koutris received the ACM SIGMOD Best Dissertation Award in 2006, 2010, and 2016 respectively, and Nilesh Dalvi was a runner up in 2008.