Explainable AI, Biology & Clinical Medicine

Current Research Focus - AI, Biology, and Clinical Medicine (ABC):

• Explainable AI principles & techniques

• AI transparency & model auditing

• Alzheimer's disease therapeutic target discovery

• Biology of aging

• Early disease driver detection

• Cancer precision medicine

• Medical image AI

Overview:

The goal of Prof. Su-In Lee's lab is to fundamentally advance the way AI is integrated with biomedicine by addressing novel and forward-looking scientific questions, enabled by AI advances. 

For example, when the primary focus of AI applications in biomedicine was on making accurate predictions using machine learning (ML) models, we uniquely focused on why a certain prediction was made by developing novel explainable AI (XAI) principles, and techniques (e.g., SHAP) to improve the interpretability of ML models, applicable to a broad spectrum of problems beyond biomedicine. Our XAI techniques can address key questions in biomedicine, such as the molecular basis for complex phenotypes, design strategies for therapeutics, or the reasoning process of clinical decisions. 

Lee lab’s research focuses on a broad spectrum of problems, advancing: (A) AI/ML - developing XAI principles and techniques, (B) Biology - identifying the cause and treatment of challenging diseases such as cancer and Alzheimer’s disease, and (C) Clinical medicine & healthcare - developing and auditing clinical AI models. See our latest publications.

We are a group of passionate researchers consisting of CSE Ph.D. students and MSTP (M.D./Ph.D.) students.  Our lab is interdisciplinary with backgrounds ranging from computer science, statistics, and mathematics to molecular & cell biology and clinical medicine.