Advisory Board

Professor Lyle H. Ungar

Lyle H. Ungar, Ph.D. is Associate Professor of Computer and Information Science, Chemical and Biomolecular Engineering, Electrical and Systems Engineering in the School of Engineering and Applied Science, Operations and Information Management in the Wharton School, and Genomics and Computational Biology in the School of Medicine, all at the University of Pennsylvania.
His research group develops scalable machine learning and text mining methods, including clustering, feature selection, and semi-supervised and multi-task learning for large bioinformatic and web-based problems. Example projects include semi-supervised methods for information extraction, multi-view learning for gene expression and fMRI data, and use of document and link structure for informing feature selection or transfer of knowledge between tasks.
Lyle coauthored Clustering Methods for Collaborative Filtering, Efficient Clustering of High-Dimensional Data Sets with Application to Reference Matching, A Hybrid Neural Network-First Principles Approach to Process Modeling, Methods and Metrics for Cold-Start Recommendations, Iterative Combinatorial Auctions: Theory and Practice, and Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments.
His patents include Method and apparatus for pattern mapping system with self-reliability check, System and method for scheduling broadcast of and access to video programs and other data using customer profiles, System for generation of object profiles for a system for customized electronic identification of desirable objects, and Lempel-Ziv data compression technique utilizing a dictionary pre-filled with frequent letter combinations, words and/or phrases.
Lyle earned his B.S. from Stanford University in 1979 and his Ph.D. from M.I.T. in 1984. He has received the Presidential Young Investigator (PYI) award from the National Science Foundation, has published over 100 professional articles, and is co-inventor on eight patents.
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