Lee Lab

Explainable Machine Learning for Biology & Precision Medicine

Prof. Su-In Lee's lab seeks to develop interpretable machine learning techniques to learn from big data: (1) how the human genome or protein works, (2) how to improve healthcare, and (3) how to treat challenging and complex diseases such as cancer and Alzheimer's disease.

Ongoing Projects

  • Enabling precision cancer medicine and drug development (collaboration with Hematology, and Center for Cancer Innovation)
  • Seeking cure for Alzheimer's disease (with Pathology, Neuropathology, and Internal Medicine)
  • Developing interpretable deep learning techniques for genomic, multi-omic, and protein structure data
  • Predicting kidney diseases (with Kidney Research Institute)
  • Bringing ML to operating rooms (with Anesthesiology & Pain Medicine)
  • Enabling efficient pre-hospital prediction for trauma patients (with Emergency Medicine)
  • Making medical examinations more efficient (with School of Dentistry, and Global Health)
  • Developing interpretable ML principles, techniques, and theories


  • 10/10/2018: Scott's Prescience paper is published as a cover article of the October issue of Nature Biomedical Engineering.