Ben Lengerich

Ben Lengerich

Postdoctoral Fellow
MIT Computational Biology Lab (Kellis Lab)
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Massachusetts Institute of Technology (MIT)
Broad Institute of MIT and Harvard

Hello! I'm a postdoc at MIT CSAIL and the Broad Institute, advised by Manolis Kellis. I also work closely with Rich Caruana at MSR. I completed my PhD in Computer Science and MS in Machine Learning at Carnegie Mellon University, advised by Eric Xing. I received my BS in Computer Science and BS in Mathematics from Penn State in 2015.

Formal Bio

I develop machine learning methods to understand complex diseases and advance precision medicine. See here for more details.

I am on the 2023-2024 academic job market.


Email Address:

Office: D-528 Stata Center, MITMap

Schedule a time to chat.

Connect with me on Twitter or LinkedIn.


New talk: Excited to present "Contextualized learning for adaptive yet persistent AI in biomedicine" at ETH Zurich, Duke University, University of Wisconsin, Penn State, and University of Colorado!

Feb 2024

New talk: Honored to present "Beyond Zero-to-One" at Mt Sinai's AI and Human Health Seminar Series!

Dec 2023

New preprint: Our study of Contextualized Networks and implications in cancer is now available on Biorxiv!

Dec 2023

New preprint: Contextualized Machine Learning is now available on Arxiv!

Oct 2023

New preprint: Contextualized Policy Recovery is now available on Arxiv!

Oct 2023

Award: Honored to be selected as a "Rising Star in Data Science" by UChicago & UCSD!

Oct 2023


I research machine learning and computational biology, with an aim to bridge the gap between data-driven insights and actionable medical interventions. My research is financially supported by the Alana Down Syndrome Center at MIT, supporting our aims of using contextualized machine learning to understand complex diseases such as Down Syndrome.
  • Context-Adaptive Systems (Meta- and Contextualized Learning): How do we build AI agents that adapt to context?
    Selected Publications
  • Prior Knowledge as Context: Connecting Statistical Inference to Foundation Models
    Selected Publications
  • Interpretable Representations of Complex and Nonlinear Systems: How can we build models that summarize complicated patterns in interpretable ways?
    Selected Publications
  • Clinical Tools for Personalized Medicine: How can we analyze real-world evidence to improve care for every patient?
    Selected Publications
More information about these directions and publications is available here.