I’m a postdoc at MIT CSAIL in Polina Golland’s Medical Vision Group where I’m building dense representation learning and domain randomization methods for data and label-efficient learning tasks. 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 about research, feel free to reach out if our interests overlap!
Research Overview
TL;DR: Biomedical Computer Vision, Data-efficient Machine Learning, Equivariant Networks
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 procedural image renderers, enabling segmentation of any modality and organism without 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.
Recent Updates
| 02/24 | Just accepted by CVPR 2024: Our new work (led by Vivek) leverages differentiable rendering, se(3) lie algebras, self-supervised geodesic pre-training and more to get sub-millimeter accurate 2D/3D rigid registration, all in about a second. |
| 12/23 | I’m a guest on this week’s AI-ready Healthcare podcast! Listen here. |
| 12/23 | I’ll be an area chair for CHIL 2024. |
| 12/23 | Our new work (led by Benjamin) shows that intensity-canonicalization greatly improves SE(3)-equivariant rigid tracking in volumetric data. |
| 09/23 | I was an outstanding reviewer for ICCV 2023 and an honorable mention for outstanding reviewer for MICCAI 2023. |
| 07/23 | Our work on training a single model to segment 3D blob-like instances without retraining in any 3D bio-microscopy or radiology dataset is now out and accepted by WACV 2024! Try it in Colab. |
| 07/23 | Our MIDL oral papers were both selected for journal extensions in Medical Image Analysis and Nalini won best oral paper! |
| 05/23 | Our work (led by Neerav) on better calibrating neural nets for spatial tasks was early accepted to MICCAI’23! Preprint here. |
| 05/23 | I was an outstanding reviewer for CVPR 2023. |
| 03/23 | We have two papers accepted as oral talks at MIDL 2023! We build E(3) x SO(3) equivariant networks for diffusion MRI and develop data-consistent MRI motion correction. |
| 02/23 | Our work on learning probabilistic piecewise rigid atlases for model organisms used in neuroscience was accepted as an oral talk by IPMI 2023! Preprint here and code here. |
| 10/22 | I was an outstanding reviewer for ECCV 2022. |
| 10/22 | I gave a series of talks on multi-scale dense representation learning at MIT CSAIL, the Martinos Center, and Brigham and Womens Hospital. |
| 09/22 | Our work on developing multi-scale and locality-sensitive self-supervised representation learning stategies was accepted as an oral talk by NeurIPS 2022! |
| 08/22 | Our work building transformers for MRI reconstruction was accepted by WACV 2023. |
| 07/22 | Our work on unsupervised MRI reconstruction was accepted by Medical Image Analysis. |
| 06/22 | I received a student travel award for MICCAI 2022. |
| 06/22 | Our work on representation learning for multi-modality non-rigid registration was accepted to MICCAI 2022. Preprint here. |
| 05/22 | I was an outstanding reviewer for CVPR 2022. |
| 04/22 | I received the Pearl Brownstein Doctoral Research Award from NYU CSE for “doctoral research which shows the greatest promise”. |
| 07/21 | Our paper on learning deformable templates was accepted by ICCV 2021! Head over to our webpage for the preprint, code, and highlights. |