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 completed my PhD in Computer Science at CMU advised by Eric Xing. I've also been very blessed to spend time working with Rich Caruana at MSR.

I research machine learning and computational biology, with a focus on methods to understand complex diseases and improve precision medicine. I see challenges in context-specific learning, interpretable AI, and personalized and precision medicine as key to advancing the field. More info about my research can be found below; if we have overlapping interests, please feel free to reach out.

Formal Bio


Email Address:

Office: D-528 Stata Center, MITMap

Schedule a time to chat.

Connect with me on Twitter.


New preprint: We connect LLMs with GAMs - on Arxiv and PyPI!

Aug 2023

Code available: TalkToEBM is now available on PyPI!

Aug 2023

Personal Update: I am on paternity leave for the summer!

May 2023

Code available: Contextualized.ML is now available on PyPI!

Nov 2022

New preprint: Our study of biases and implications in EHR datasets ("Death by Round Numbers") is now available on Medrxiv!

May 2022

Talk: Presented Contexutalized ML for Disease Subtyping at BIRS. Video/slides available.

May 2022

New publication: Our study of Personalized Treatment Effects in Covid-19 is now published in JBI!

May 2022


My research is currently 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.

My current research foci include:

  • Context-Adaptive Systems (Meta- and Contextualized Learning): Can we build AI agents which adapt to different contexts?
    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 deliver optimal care for every individual patient?
    Selected Publications
More information about these directions and publications is available here.