BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Information Systems Group - ECPv6.4.0.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Information Systems Group
X-ORIGINAL-URL:https://isg.ics.uci.edu
X-WR-CALDESC:Events for Information Systems Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220422T110000
DTEND;TZID=America/Los_Angeles:20220422T120000
DTSTAMP:20260711T044930
CREATED:20220324T185928Z
LAST-MODIFIED:20260405T013443Z
UID:1413-1650625200-1650628800@isg.ics.uci.edu
SUMMARY:Tim Kraska (MIT): Towards instance-optimized data systems
DESCRIPTION:Location:  \nDBH 6011 \nhttps://uci.zoom.us/j/94559511434 (for UCI users only) \n\nSpeaker: Tim Kraska\, MIT \nAbstract: Recently\, there has been a lot of excitement around ML-enhanced (or learned) algorithms and data structures. For example\, there has been work on applying machine learning to improve query optimization\, indexing\, storage layouts\, scheduling\, log-structured merge trees\, sorting\, compression\, sketches\, among many other data management tasks. Arguably\, the ideas behind these techniques are similar: machine learning is used to model the data and/or workload in order to derive a more efficient algorithm or data structure. Ultimately\, what these techniques will allow us to build are “instance-optimized” systems; systems that self-adjust to a given workload and data distribution to provide unprecedented performance and avoid the need for tuning by an administrator. \nIn this talk\, I will first provide an overview of the opportunities and limitations of current ML-enhanced algorithms and data structures\, present initial results of SageDB\, a first instance-optimized system we are building as part of DSAIL@CSAIL at MIT\, and finally outline remaining challenges and future directions. \nTim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT’s Computer Science and Artificial Intelligence Laboratory\, co-director of the Data System and AI Lab at MIT (DSAIL@CSAIL)\, and co-founder of Einblick Analytics. Currently\, his research focuses on building systems for machine learning\, and using machine learning for systems. Before joining MIT\, Tim was an Assistant Professor at Brown\, spent time at Google Brain\, and was a PostDoc in the AMPLab at UC Berkeley after he got his PhD from ETH Zurich. Tim is a 2017 Alfred P. Sloan Research Fellow in computer science and received several awards including the VLDB Early Career Research Contribution Award\,  the VMware Systems Research Award\, the university-wide Early Career Research Achievement Award at Brown University\, an NSF CAREER Award\, as well as several best paper and demo awards at VLDB\, SIGMOD\, and ICDE.
URL:https://isg.ics.uci.edu/event/tim-kraska-mit-towards-instance-optimized-data-systems/
LOCATION:DBH 6011
END:VEVENT
END:VCALENDAR