Lyle H. Ungar
Associate Professor
Computer and Information Science (CIS)
Honors and Awards: NSF Presidential Young Investigator Award
Research Expertise: Machine Learning | Bioinformatics
Lyle develops machine learning and text mining methods in order to solve problems in genomics, bioinformatics, and information extraction. Key to his work in building scalable solutions to such problems is the development of methods that can exploit knowledge, including the link structure between documents or genes and the structure of the relational database training from which data are drawn. Lyle and his group are also developing theoretically grounded feature selection methods that scale to millions of features, and scalable clustering and regression algorithms.
Member of:- Penn Research in Machine Learning (PRiML)
- Penn Center for Bioinformatics (PCBi)
- Institute for Research in Cognitive Science (IRCS)
Affiliations: Associate Professor of Operations and Information Management (Wharton); Associate Professor of Genomics and Computational Biology (SOM)
Education:
PhD 1984 - Massachusetts Institute of Technology
BS 1979 - Stanford University
Recent Publications 
- The marketcast method for aggregating prediction market forecasts, Atanasov, P. | Rescober, P. | Stone, E. | Servan-Schreiber, E. | Mellers, B. | Tetlock, P. | Ungar, L., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013
- Online discussion of drug side effects and discontinuation among breast cancer survivors, Mao, J.J. | Chung, A. | Benton, A. | Hill, S. | Ungar, L. | Leonard, C.E. | Hennessy, S. | Holmes, J.H., Pharmacoepidemiology and Drug Safety, 2013
- Improving supervised sense disambiguation with web-scale selectors, Andrew Schwartz, H. | Gomez, F. | Ungar, L.H., 24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers, 2012
- Extracting templates from radiology reports using sequence alignment, Wu, S. | Langlotz, C.P. | Lakhani, P. | Ungar, L.H., International Journal of Data Mining and Bioinformatics, 2012
- Characterizing emergence using a detailed micro-model of scien Investigating two hot topics in nanotechnology, Boyack, K.W. | Klavans, R. | Small, H. | Ungar, L., 2012 Proceedings of Portland International Center for Management of Engineering and Technology: Technology Management for Emerging Technologies, PICMET'12, 2012