Ben Lengerich

Research Interests and Selected Publications

Word Cloud scraped from Research Papers

I research machine learning methods and build artificial intelligence systems to automate the process of scientific discovery. I am particularly interested in questions of automatically identifying and adapting to changing contexts. This research focus requires advances in interpretable machine learning, multi-task learning, and task representation learning, and finds natural applications in precision medicine and computational genomics of complex diseases such as Alzheimer’s Disease, Down Syndrome, and cancer.

Context-Specific Inferences: What happens if we build models which can adapt to different contexts?
  • Code: Contextualized.ML Python package
  • NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
    Benjamin Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis
    Abstract Pre-print
    Arxiv 2021
  • Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning
    Benjamin J. Lengerich*, Maruan Al-Shedivat*, Amir Alavi, Jennifer Williams, Sami Labbaki, Eric P. Xing
    Abstract Pre-print
    Medrxiv 2020
Interpretable AI: How can we build models that summarize patterns in ways that we can use to understand the underlying phenomena?
  • Neural Additive Models: Interpretable Machine Learning with Neural Nets.
    Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Geoffrey Hinton, Rich Caruana
    Abstract Pre-print Paper Cite
    NeurIPS 2021
  • Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
    Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
    Abstract Pre-print Paper Cite
    AISTATS 2020
  • Code: Interpret.ML Python package.
Personalized and Precision Medicine: How can we deliver optimal care for every individual patient?
  • Automated Interpretable Discovery of Heterogeneous Treatment Effectiveness: A COVID-19 Case Study
    Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Caleb Ellington, Rich Caruana
    Abstract Pre-print Paper Cite
    JBI 2022
Computational Genomics of Complex Diseases: What are the biological causes, implications, and therapeutics targets of complex diseases?

Full List of Publications

Possibly more updated lists can be found on Google Scholar, Semantic Scholar, or DBLP.