My research focuses on creating label-efficient and explainable deep learning models for medical imaging to improve clinical workflow and patient care.In particular, I am excited about exploring the following directions:

• Multimodal learning: How can we efficiently integrate information from different modalities (e.g. imaging, text, tabular data, sequential data) to better inform clinical decisions?
• Explainable AI: Can we enable computer aided diagnosis systems to explain their reasoning in a way that mimics how humans communicate?
• Human-AI collaboration: What is the optimal way to present an AI’s diagnosis to clinicians so they can collaborate effectively to maximize patient outcomes?

I received my Ph.D. at the NYU Center for Data Science, advised by Prof. Krzysztof J. Geras and Prof. Kyunghyun Cho. Prior to joining NYU, I worked at Two Sigma Investments. I hold a Bachelor’s degree in Computer Science from Rice University.

Perspective students: Starting in 2024, I’m looking for motivated Ph.D. students to join my team. Please check out my advising statement . I’m also open to collaborations with predoctoral researchers and visiting scholars. If you are interested in working with me, please drop me an email.