I am a PhD candidate at MIT CSAIL advised by Polina Golland and a student researcher at Google DeepMind with Daniel Duckworth. My research focuses on computer vision, particularly representations of 3D scenes. I also work on medical image analysis, including robust and interpretable techniques for both generative and discriminative models. I use diffusion models to make things.

I interned in the Computational Photography Group at Adobe working with Jiawen Chen and Cecilia Zhang. I previously worked with Jim Duncan and Julius Chapiro in the Yale Radiology Research Lab. I am supported by the Takeda Fellowship and Siebel Scholarship. I received a B.S. in Biomedical Engineering at Yale, where I did research with Stuart Campbell.

Selected Publications

Discretization Invariant Networks for Learning Maps between Neural Fields
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Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
Paper | Code
Interpolating between Images with Diffusion Models
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Spatial-Intensity Transforms for Medical Image-to-Image Translation
Project | Paper | Code
Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series
Paper | Code
Geometry-Aware Field-to-Field Transformations for 3D Semantic Segmentation
Project | Paper | Code
Pre-Trained Language Models for Interactive Decision-Making
Project | Paper | Code
Approximate Discretization Invariance for Deep Learning on Neural Fields
Paper | 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
Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation
Project | Paper | Video | Code
Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer
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A probabilistic approach for interpretable deep learning in liver cancer diagnosis
Project | Paper | Talk | Code
Deep learning for liver tumor diagnosis part II: interpretable deep learning to characterize tumor features
Project | 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

Fun AI Creations (more)

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