Events

Contextualized learning for adaptive yet persistent AI in biomedicine

Speaker: Ben Lengerich

Location: 60 Fifth Avenue, Room 150
Videoconference link: https://nyu.zoom.us/j/91378603321

Date: Monday, April 1, 2024

Abstract:

Machine learning models trained in one biomedical context tend to not generalize to other contexts. As a result, actual performance of ML models often lags advertised performance. This presentation will examine “contextualized learning”, a meta-learning paradigm which seeks to share information between distinct contexts by learning meta-relationships between dataset context and statistical parameters. Using contextualized network inference as an illustrative example, I will show how this approach 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.

 

Speaker Bio:

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, selection as a "Rising Star in Data Science” by the University of Chicago and UC San Diego, and "Next Generation in Biomedicine" by the Broad Institute.