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X-WR-CALNAME:Information Systems Group
X-ORIGINAL-URL:https://isg.ics.uci.edu
X-WR-CALDESC:Events for Information Systems Group
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DTSTART:20190310T100000
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DTSTART:20191103T090000
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DTSTART;TZID=America/Los_Angeles:20190201T110000
DTEND;TZID=America/Los_Angeles:20190201T120000
DTSTAMP:20261108T035905
CREATED:20190125T020959Z
LAST-MODIFIED:20190125T211117Z
UID:1045-1549018800-1549022400@isg.ics.uci.edu
SUMMARY:Prof. Jeff Ullman: Data Science:  Is it Real?
DESCRIPTION:ABSTRACT: \nWe shall discuss the various ways in which data science is approached by different communities\, including the Statistics\, Machine-Learning\, and Database communities.  Each presents a different viewpoint and values different outcomes. Some consequences of these approaches will be discussed. As an example\, of why data science is not machine learning\, we shall sketch two important algorithms not in this class: locality-sensitive hashing and approximate counting. \nBIOGRAPHY: \nJeffrey David Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp.  He received the B.S. degree in Engineering Mathematics from Columbia in 1963 and the PhD in Electrical Engineering from Princeton in 1966. Prior to his appointment at Stanford in 1979\, he was a member of the technical staff of Bell Laboratories from 1966-1969\, and on the faculty of Princeton University between 1969 and 1979.  From 1990-1994\, he was chair of the Stanford Computer Science Department. Prof. Ullman was elected to the US National Academy of Engineering in 1989\, the American Academy of Arts and Sciences in 2012\, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996)\, the ACM Karl V. Karlstrom Outstanding Educator Award (1998)\, the Knuth Prize (2000)\, the Sigmod E. F. Codd Innovations award (2006)\, the IEEE von Neumann medal (2010)\, and the NEC C&C Foundation Prize (2017).  He is the author of 16 books\, including books on database systems\, data mining\, compilers\, automata theory\, and algorithms. \nURL:  i.stanford.edu/~ullman
URL:https://isg.ics.uci.edu/event/prof-jeff-ullman-visit/
LOCATION:DBH 6011
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DTSTART;TZID=America/Los_Angeles:20190208T150000
DTEND;TZID=America/Los_Angeles:20190208T160000
DTSTAMP:20261108T035905
CREATED:20190125T030748Z
LAST-MODIFIED:20190125T210119Z
UID:1054-1549638000-1549641600@isg.ics.uci.edu
SUMMARY:Prof. Sang-Woo Jun: Lowering the cost of large-scale data analytics via efficient use of flash storage
DESCRIPTION:In this talk\, I present the storage systems aspect of the ongoing work on using relatively cheap solid-state secondary storage to replace expensive DRAM for analytics on large amounts of data\, using as examples graph analytics and the bioinformatics application somatic mutation finding.\nBoth applications are inherently random access intensive\, which is a bad fit with the coarse access granularity of secondary storage.\nIn both cases\, performance impact of random access is addressed in two ways: reorganizing the computation and access pattern to remove random access\, and modifying the internals of the storage device controller to optimize for random accesses.\nIn both cases\, a desktop-class system with secondary storage was able to match the performance of a system with an order of magnitude higher cost.\n\n\n\nBio:\n\nSang-Woo Jun is a faculty member at Donald Bren School of Information and Computer Sciences at the University of California\, Irvine. His research focus is on computer architecture and system design for lowering the cost of handling large amounts of data. Before joining UCI\, Sang-Woo did his Ph.D with Professor Arvind at the Massachusetts Institute of Technology\, and his B.S. at Seoul National University in Korea. His industry experience includes intern at the Big Data Discovery team at Oracle\, and Server/Client developer at Nexon inc.
URL:https://isg.ics.uci.edu/event/prof-sang-woo-jun-lowering-the-cost-of-large-scale-data-analytics-via-efficient-use-of-flash-storage/
LOCATION:DBH 3011
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