I am a PhD candidate at MIT CSAIL advised by Polina Golland. My research focuses on medical image analysis, particularly robust and interpretable techniques for both generative and discriminative models. My projects have included analyzing brain MRI (acute ischemic stroke, neurodegenerative disease), liver MRI (hepatocellular carcinoma), fetal MRI (placental oxygenation), and colonoscopy videos (inflammatory bowel disease). I also work on 3D vision, including neural fields and trajectory estimation.

I previously worked with Jim Duncan and Julius Chapiro in the Yale Radiology Research Lab, and interned at Iterative Scopes. 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.


Approximate Discretization Invariance for Deep Learning on Neural Fields
Project | Paper | Video | Code
Automatic Segmentation of the Placenta in BOLD MRI Time Series
Paper | Code
Pre-Trained Language Models for Interactive Decision-Making
Project | 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
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
Paper | Code
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
Slowing of contractile kinetics by myosin-binding protein C can be explained by its cooperative binding to the thin filament

Selected Awards

  • Takeda Fellowship, 2021-2022
  • Siebel Foundation Scholar, 2020
  • Yale Department of Biomedical Engineering Prize, 2015
  • Tau Beta Pi Engineering Honor Society, 2015


  • Advances in Computer Vision (6.819/6.869), MIT, Spring 2021
    Teaching Assistant with Prof. Bill Freeman and Phillip Isola
  • Undergraduate Mentor, MIT Undergraduate Research Opportunities Program

Academic Service

  • Program Committee, Medical Imaging Meets NeurIPS Workshop (MedNeurIPS)
  • Reviewer, Medical Image Analysis (MedIA)
  • Reviewer, Conference on Neural Information Processing Systems (NeurIPS)
  • Reviewer, Medical Image Computing and Computer Assisted Intervention (MICCAI)

Invited Talks

  • Boston Medical Imaging Workshop 2022
    Robust counterfactual image generation with spatial-intensity transforms
  • MIT-Takeda Presentation Series 2022
    Identifying radiological biomarkers with generative models