Lyle Ungar

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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]


  1. ^ a b c d "Lyle H. Ungar". Retrieved May 6, 2014.
  2. ^ "Lyle H. Ungar". Psychology Department, University of Pennsylvania. May 6, 2014.
  3. ^ "Lyle Ungar". Retrieved May 7, 2014.
  4. ^ a b "The Team". The Good Judgment Project. Retrieved May 6, 2014.
  5. ^ "Blog posts by Lyle Ungar". The Good Judgment Project. Retrieved May 7, 2014.
  6. ^ Muehlhauser, Luke (March 26, 2014). "Lyle Ungar on forecasting". Machine Intelligence Research Institute. Retrieved May 6, 2014.
  7. ^ "Lyle H. Ungar" (PDF). Annenberg Public Policy Center. Retrieved May 7, 2014.
  8. ^ "Science Advisory Board". Spark Park.

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