Lise Getoor

Lise Getoor is a professor in the Computer Science Department,[1] at the University of California, Santa Cruz,[2] and an adjunct professor in the Computer Science Department[3] at the University of Maryland, College Park.[4] Her primary research interests are in machine learning and reasoning with uncertainty, applied to graphs and structured data. She also works in data integration, social network analysis and visual analytics. She has edited a book on Statistical relational learning that is a main reference in this domain.[5] She has published many highly cited papers in academic journals and conference proceedings.[6][7][8][9] She has also served as action editor for the Machine Learning Journal, JAIR associate editor, and TKDD associate editor. She is a board member of the International Machine Learning Society, has been a member of AAAI Executive council, was PC co-chair of ICML 2011, and has served as senior PC member for conferences including AAAI, ICML, IJCAI, ISWC, KDD, SIGMOD, UAI, VLDB, WSDM and WWW.

Lise Getoor
Photo was taken in 2011
Born
Seattle, WA
NationalityAmerican
CitizenshipAmerican
Alma materStanford University
Known forStatistical relational learning, Link mining, Probabilistic soft logic
AwardsAAAI Fellow (2013)
ACM Fellow (2019)
Scientific career
FieldsComputer Science, Machine Learning, Data Mining, and Statistical relational learning
InstitutionsUniversity of California, Santa Cruz, and University of Maryland, College Park
Doctoral advisorDaphne Koller
Other academic advisorsStuart Russell
Websitegetoor.soe.ucsc.edu

She received her Ph.D. from Stanford University,[10] her M.S. from UC Berkeley, and her B.S. from UC Santa Barbara. Prior to joining University of California, Santa Cruz, she was a professor at the University of Maryland, College Park until Nov 2013.[11]

Recognition

Getoor has multiple best paper awards, an NSF Career Award, and is an Association for the Advancement of Artificial Intelligence (AAAI) Fellow.[12] She was elected as an ACM Fellow in 2019 "for contributions to machine learning, reasoning under uncertainty, and responsible data science".[13]

References

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