Computational Research Scientist
Dr. Kathryn Linehan is a Computational Research Scientist within the Data Analytics Center (DAC). One of the primary missions of the DAC is to provide UVA researchers a platform for their data management and analysis, specifically ‘big data’ analysis, by utilizing machine learning (ML) and artificial intelligence (AI) approaches. Dr. Linehan has a demonstrated track record in the AI/ML field including natural language processing (NLP), multiple ML and deep learning techniques such as neural networks and LLMs, and high performance computing. Prior to joining the DAC, she applied her AI/ML skillsets within the Social and Decision Analytics Division at UVA’s Biocomplexity Institute where she was employed as a Research Scientist. She is an applied mathematician by training and has a PhD in Applied Mathematics, Statistics and Scientific Computation from the University of Maryland. In addition to AI/ML based approaches, her research interests include numerical linear algebra, especially low-rank matrix approximations and their applications.
Selected Publications
- K Linehan and R Balan, “CUR matrix approximation through convex optimization for feature selection,” Frontiers in Applied Mathematics and Statistics 11:1632218 (2025). doi: 10.3389/fams.2025.1632218
Under Review
- K Linehan and R Balan, “Approximation of the Proximal Operator of the L-infinity Norm Using a Neural Network,” arXiv:2408.11211 [math.NA, cs.LG]
- JM Erno, K Linehan, AB Danquah, RCD Buerlein, VM Shami, D Strand, A Wang, and A Podboy, “Utilizing machine learning to identify predictors of post-ERCP pancreatitis”
Education
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Ph.D. Applied Mathematics & Statistics, and Scientific Computation
University of Maryland, College Park (May 2024)
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Certificate in Data Science
Georgetown University, School of Continuing Studies (2018)
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M.S. Applied Mathematics & Statistics, and Scientific Computation
University of Maryland, College Park (2009)
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B.A. Mathematics
Hood College (2006)
