Interpretable AI for Cardiovascular & Population Health

Building trustworthy machine learning systems for high-stakes healthcare settings. Combining multimodal ECG analysis, explainable AI methods, and computational epidemiology to detect risk earlier, characterize physiologic mechanisms, and ensure model decisions are transparent to clinicians and public health leaders.

Research Publications Lab

Research Focus

Multimodal AI & Signal Processing

Design and evaluate fusion architectures that combine time, frequency, and time-frequency ECG representations with clinical data. Develop methods that preserve complementary diagnostic information across modalities.

Robustness & Physiologic Invariance

Study when models should or should not change their predictions under natural physiologic drift. Develop metrics and training strategies that maintain stability under benign perturbations while remaining sensitive to pathological changes.

Explainability & Clinical Translation

Create interpretable models that communicate evidence in forms clinicians and public-health leaders can interrogate, critique, and refine. Ensure medical plausibility and trustworthiness in high-stakes healthcare decisions.

Key Statistics

30+

Research Publications

20+

Multimodal AI Systems

2M+

ECG & Physiologic Samples

10+

Years

Research Overview

Research Vision

My research focuses on developing AI systems that can be trusted in high-stakes cardiovascular and population-health settings. I design multimodal models that integrate time, frequency, and time-frequency ECG representations with clinical and population data to detect risk earlier and support transparent decision-making. Across projects, I study when models should or should not change their predictions, how to preserve physiologic invariance under noise and drift, and how to communicate evidence in forms that clinicians and public-health leaders can interrogate, critique, and refine.

Teaching Philosophy

My teaching philosophy is grounded in the belief that active engagement leads to deeper learning. I design learning experiences that emphasize practical application, analytical reasoning, and hands-on problem solving. By connecting technical concepts to real organizational and industry contexts and helping students to develop both competence and confidence. I continuously work to enhance the learning environment through thoughtful curriculum improvement and evidence-based instructional practices. I also champion computer science for all and strive to ensure that every student has equitable access to high-quality computing education.