BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Computer Science - ECPv5.12.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Computer Science
X-ORIGINAL-URL:https://www.cs.jhu.edu
X-WR-CALDESC:Events for Department of Computer Science
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191010T104500
DTEND;TZID=America/New_York:20191010T114500
DTSTAMP:20220112T094333
CREATED:20210629T210720Z
LAST-MODIFIED:20210629T210720Z
UID:1962335-1570704300-1570707900@www.cs.jhu.edu
SUMMARY:Gerald M. Masson Distinguished Lecture Series: Lise Getoor\, University of California\, Santa Cruz – “The Unreasonable Effectiveness of Structure”
DESCRIPTION:LocationHackerman Hall B-17AbstractOur ability to collect\, manipulate\, analyze\, and act on vast amounts of data is having a profound impact on all aspects of society. Much of this data is heterogeneous in nature and interlinked in a myriad of complex ways. From information integration to scientific discovery to computational social science\, we need machine learning methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. Statistical relational learning (SRL) is a subfield that builds on principles from probability theory and statistics to address uncertainty while incorporating tools from knowledge representation and logic to represent structure. In this talk\, I will give a brief introduction to SRL\, present templates for common structured prediction problems\, and describe modeling approaches that mix logic\, probabilistic inference and latent variables. I’ll overview our recent work on probabilistic soft logic (PSL)\, a SRL framework for large-scale collective\, probabilistic reasoning in relational domains. I’ll close by highlighting emerging opportunities (and challenges!!) in realizing the effectiveness of data and structure for knowledge discovery.BioLise Getoor is a professor in the Computer Science Department at the University of California\, Santa Cruz and director of the Data\, Discovery and Decisions Research Center at UC Santa Cruz.  Her research areas include machine learning\, data integration and reasoning under uncertainty\, with an emphasis on graph and network data. She has over 250 publications and extensive experience with machine learning and probabilistic modeling methods for graph and network data. She is a Fellow of the Association for Artificial Intelligence\, an elected board member of the International Machine Learning Society\, serves on the board of the Computing Research Association (CRA)\, and was co-chair for ICML 2011.  She is a recipient of an NSF Career Award and thirteen best paper and best student paper awards.  She received her PhD from Stanford University in 2001\, her MS from UC Berkeley\, and her BS from UC Santa Barbara\, and was a professor in the Computer Science Department at the University of Maryland\, College Park from 2001-2013.HostXin JinVideoWatch seminar video.
URL:https://www.cs.jhu.edu/event/gerald-m-masson-distinguished-lecture-series-lise-getoor-university-of-california-santa-cruz-the-unreasonable-effectiveness-of-structure/
END:VEVENT
END:VCALENDAR