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 Fall 2026, I am looking for highly motivated Ph.D. students to join my research group. You may apply through NYU Data Science PhD (see the medical AI track) or the Biomedical Imaging & Technology PhD at the NYU Vilcek Institute. For more details, 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.

Our work explores exciting frontiers in AI and medical imaging, including:

• Developing multi-modal deep learning models that integrate medical imaging (across multiple modalities) with clinical data such as EHRs and lab results.
• Designing counterfactual deep learning approaches to optimize diagnostic and treatment pathways.
• Building AI systems for video analysis, with applications such as understanding infant psychology development.