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 compute-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
I exploit the invariances and equivariances inherent to biomedical imaging tasks to simplify learning representations in data and annotation-starved biomedical environments. This includes:
- Dense multi-scale representation learning methods for key image-to-image neuroimaging tasks such as multi-modality non-rigid registration and one-shot segmentation and
- Constructing equivariant architectures for data-efficient GANs applied to digital pathology and sparse spatio-spherical deconvolution problems in diffusion MRI.
I also work on generative models for learning deformable templates of biomedical populations and enjoy collaborating broadly across inverse problems in radiology and bio-microscopy.
Recent Updates
| 09/23 | I was an outstanding reviewer for ICCV 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. |