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  • CS Colloquium: Ben Lengerich (MIT) - Contextualized learning for adaptive yet persistent AI in biomedicine

    Thu, Mar 07, 2024 @ 10:00 AM - 11:00 AM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Ben Lengerich, MIT

    Talk Title: Contextualized learning for adaptive yet persistent AI in biomedicine

    Series: Computer Science Colloquium

    Abstract: Machine learning models often exhibit diminished generalizability when applied across diverse biomedical contexts (e.g., across health institutions), leading to a significant discrepancy between expected and actual performance. To address this challenge, this presentation introduces "contextualized learning", a meta-learning paradigm designed to enhance model adaptability by learning meta-relationships between dataset context and statistical parameters. Using network inference as an illustrative example, I will show how contextualized learning estimates context-specific graphical models, offering insights such as personalized gene expression analysis for cancer subtyping. The talk will also discuss trends towards “contextualized understanding”, bridging statistical and foundation models to standardize interpretability. The primary aim is to illustrate how contextualized learning and understanding contribute to creating learning systems that are both adaptive and persistent, facilitating cross-context information sharing and detailed analysis.
    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Ben Lengerich is 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 is advised by Manolis Kellis. His research in machine learning and computational biology 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. He holds a PhD in Computer Science and MS in Machine Learning from Carnegie Mellon University, where he was advised by Eric Xing. 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 and UC San Diego.

    Host: Willie Neiswanger

    Location: Olin Hall of Engineering (OHE) - 136

    Audiences: Everyone Is Invited

    Contact: CS Faculty Affairs


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