Assistant Professor

Department of Information Sciences and Technology

College of Engineering & Computing

George Mason University

Dr. Kevin Lybarger is a tenure-track Assistant Professor in the Department of Information Sciences and Technology at George Mason University, specializing in machine learning and natural language processing (NLP). He has an established track record in clinical informatics, focusing on information extraction from clinical text to facilitate secondary use applications. His methodological contributions include the design of annotation schemas and taxonomies, the creation of high-quality annotated datasets, and the development of novel task-specific NLP architectures. He earned a PhD in Electrical and Computer Engineering from the University of Washington, MS in Electrical and Computer Engineering from the University of Colorado Boulder, and BS in Electrical and Computer Engineering from Seattle University. He was a Postdoctoral Fellow at the University of Washington School of Medicine through the National Library of Medicine Biomedical Informatics Research Trainee Program. For details, please see my CV.

Mason Website: https://www.gmu.edu/profiles/klybarge

Kevin Lybarger, PhD

Current affiliations

  • University of Washington School of Medicine

    I am an Affiliate Assistant Professor in the University of Washington (UW) School of Medicine, to facilitate collaboration between UW and GMU.

  • UW BioNLP

    I collaborate with the University of Washington Biomedical Language Processing (UW BioNLP) group. UW BioNLP builds systems and tools to process the free-text in the biomedical literature and clinical notes.

Former affiliations

  • TIAL

    As a doctoral student, I was part of the Transformation, Interpretation and Analysis of Language (TIAL) Group at UW. TIAL develops computational, data-driven models of language to solve the challenging problems in speech and language interpretation and analysis.

  • UW NLP

    During my PhD, I was a member of the UW NLP group, which explores a wide range of core NLP problems and emerging challenges. UW NLP is highly collaborative and leverages expertise from a across the UW campus and industry partners.