I am a graduate student at MIT CSAIL studying medical vision, advised by Polina Golland. My interests include implicit neural representations and robust deep learning. I also build models for colonoscopy video analysis at Iterative Scopes. I am supported by the Takeda Fellowship and Siebel Scholarship.

I previously worked with Jim Duncan and Julius Chapiro in the Yale Radiology Research Lab, building interpretable neural networks for liver cancer diagnosis. I received a B.S. in Biomedical Engineering at Yale, where I developed computational models of heart muscle contraction under Stuart Campbell.

Publications

Deep Learning on Implicit Neural Datasets
Paper
Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation
Paper | Talk | Slides | Code
Pre-Trained Language Models for Interactive Decision-Making
Paper | Project | Code
Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver
Paper | Code
Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer
Paper | Code
A probabilistic approach for interpretable deep learning in liver cancer diagnosis
Paper | Talk | Slides | Code
Deep learning for liver tumor diagnosis part II: interpretable deep learning to characterize tumor features
Paper | Code
Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI
Paper | Code
The Role of Artificial Intelligence in Interventional Oncology: A Primer
Paper
Slowing of contractile kinetics by myosin-binding protein C can be explained by its cooperative binding to the thin filament
Paper

Teaching

Advances in Computer Vision (6.819/6.869), MIT
Teaching Assistant with Prof. Bill Freeman and Phillip Isola
Spring 2021