Ben Lengerich
Assistant Professor
Department of StatisticsDepartment of Computer Sciences
University of Wisconsin-Madison
I develop ML methods and AI systems to advance precision medicine.
See my research group website for more details and open positions.
Contact
Email Address:
Office: 7278 Medical Sciences Center, UW-Madison
Connect with me on X (Twitter) or LinkedIn.
Biography
Ben Lengerich is an Assistant Professor at the University of Wisconsin-Madison, appointed in the Department of Statistics and by courtesy in the Department of Computer Sciences. His research emphasizes the use of context-adaptive models to understand complex diseases and advance precision medicine. Through his work, Ben aims to bridge the gap between data-driven insights and actionable medical interventions. Ben holds a PhD in Computer Science and MS in Machine Learning from Carnegie Mellon University, where he was advised by Eric Xing. Prior to joining the University of Wisconsin, he was a Postdoctoral Associate and Alana Fellow at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Broad Institute of MIT and Harvard, where he was advised by Manolis Kellis. His work has been recognized with spotlight presentations at conferences including NeurIPS, ISMB, AMIA, and SMFM, financial support from the Alana Foundation, and recognition as a "Rising Star in Data Science” by the University of Chicago & UC San Diego's Data Science Institutes.Research Interests and Selected Publications
I research machine learning and computational biology, with an aim to bridge the gap between data-driven insights and actionable medical interventions. Ongoing projects include:-
Context-Adaptive Systems (Meta- and Contextualized Learning): How do we build AI agents that adapt to context?
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Prior Knowledge as Context: Connecting Statistical Inference to Foundation Models
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Interpretable Representations of Complex and Nonlinear Systems: How can we build models that summarize complicated patterns in interpretable ways?
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Clinical Tools for Personalized Medicine: How can we analyze real-world evidence to improve care for every patient?