I’m a postdoc at MIT CSAIL in Polina Golland’s Medical Vision Group where I work on representation learning frameworks for zero-shot generalization to entirely new tasks, biomedical contexts, and datasets. I want to build machine learning methods that biomedical domain experts can use as-is without needing to retrain or adapt to their use-cases.

I got my Ph.D. from NYU under Guido Gerig at the VIDA Center where I worked on generative models and inverse problems in medical image analysis contexts.

I’m always happy to talk, feel free to reach out if our interests overlap!

Research Overview

I tackle the pervasive lack of biomedical training data and supervision to create models that generalize to new biomedical settings with minimal to no new training data.

This has included:

• Learning representations from randomized image synthesizers, enabling zero-shot generalization to any biomedical modality and organism without any domain-speicific training data.
• Developing equivariant layers for sparse spatio-spherical deconvolution in diffusion MRI and creating data-efficient GANs for digital pathology with limited annotated subjects.
• Introducing dense multi-scale representation learning methods for neuroimaging tasks such as multi-modality non-rigid registration and one-shot segmentation in settings with ample unannotated training images.

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