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PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:IC Colloquium : Big Graph Data Science
DTSTART:20141103T161500
DTEND:20141103T173000
DTSTAMP:20260407T043100Z
UID:0da8b8efe57ce1e79d8a19ffa32c19ea3b3a734a1dcb57745fbbb96d
CATEGORIES:Conferences - Seminars
DESCRIPTION:By : Lise Getoor - University of MarylandVideo of her talkAbst
 ract :\nOne of the challenges in big data analytics lies in being able to 
 reason collectively about extremely large\, heterogeneous\, incomplete\, n
 oisy interlinked data.  We need data science techniques which an represen
 t and reason effectively with this form of rich and multi-relational graph
  data.  In this talk\, I will describe some common collective inference p
 atterns needed for graph data including: collective classification (predic
 ting missing labels for nodes in a network)\, link prediction (predicting 
 potential edges)\, and entity resolution (determining which nodes refer to
  the same underlying entity).  I will describe three key capabilities req
 uired:  relational feature construction\, collective inference\, and lift
 ed reasoning.   Finally\, I will describe some of the cutting edge analy
 tic tools being developed within the machine learning\, AI\, and database 
 communities to address these challenges.  In particular\, I will describe
  work by my group on Probabilistic Soft Logic (http://psl.umiacs.umd.edu/)
 \, a highly scalable declarative language for collective inference problem
 s.Bio :\nLise Getoor is a professor in the Computer Science Department at 
 UC Santa Cruz. Her research areas include machine learning and reasoning u
 nder uncertainty\; in addition she works in data management\, visual analy
 tics and social network analysis. She has over 200 publications and extens
 ive experience with machine learning and probabilistic modeling methods fo
 r graph and network data. She is a Fellow of the Association for Artificia
 l Intelligence\, an elected board member of the International Machine Lear
 ning Society\, has served as Machine Learning Journal Action Editor\, Asso
 ciate Editor for the ACM Transactions of Knowledge Discovery from Data\, J
 AIR Associate Editor\, and she has served on the AAAI Council. She was co-
 chair for ICML 2011\, and has served on the PC of many conferences includi
 ng the senior PC of AAAI\, ICML\, KDD\, UAI\, WSDM and the PC of SIGMOD\, 
 VLDB\, and WWW. She is a recipient of an NSF Career Award and eight best p
 aper and best student paper awards. She was recently recognized as one of 
 the top ten emerging researchers leaders in data mining and data science b
 ased on citation and impact\, according to KDD Nuggets. She received her P
 hD from Stanford University in 2001\, her MS from UC Berkeley\, and her BS
  from UC Santa Barbara\, and was a professor at the University of Maryland
 \, College Park from 2001-2013.More information
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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