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American computer scientist From Wikipedia, the free encyclopedia
Lyle H. Ungar is a machine learning researcher and professor of Computer and Information Science at the University of Pennsylvania.[1] He is also affiliated with the psychology department at the university.[2]
Ungar's published research has been primarily in the area of machine learning, specifically text mining.[3][1] According to his website, his research group "develops scalable machine learning and text mining methods, including clustering, feature selection, and semi-supervised and multi-task learning for natural language, psychology, and medical research. Example projects include spectral learning of language models, multi-view learning for gene expression and MRI data, and mining social media to better understand personality and well-being."[1]
Ungar has also done some research in the domain of forecasting, in connection with his membership in The Good Judgment Project, a collaborator of the Aggregative Contingent Estimation (ACE) program of the Intelligence Advanced Research Projects Agency (IARPA).[4][5][6]
Ungar is a member of many associations and bodies devoted to advancing machine learning and related areas. These include the Annenberg Public Policy Center,[7] Center for Cognitive Neuroscience, and Institute for Research in Cognitive Science.[1] He is also a member of The Good Judgment Project.[4] He is also a science advisory board member at Spark Park.[8]
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