Starting Fall 2022, I’m a postdoc at MIT CSAIL where I’m working in Polina Golland’s group working on ML for biomedical challenges.

I obtained my Ph.D. from New York University and was advised by Guido Gerig, where I was part of the wonderful and multi-disciplinary Visualization, Imaging, and Data Analysis (VIDA) Center.

My research primarily focuses on methods development for medical computer vision and healthcare applications - a field that fortunately offers both fun technicality and real-world relevance. Particular topics of interest include equivariant networks, image synthesis, geometric deep learning, representation learning, and deformable registration.

Recent Updates

03/23We have two papers accepted as tentative oral talks at MIDL 2023! We build E(3) x SO(3) equivariant networks for diffusion MRI in one (preprint+code out soon) and develop data-consistent MRI motion correction methods in another.
02/23Our work on learning probabilistic piecewise rigid atlases for model organisms used in neuroscience was accepted by IPMI 2023! Preprint and code out soon.
10/22I was an outstanding reviewer for ECCV 2022.
10/22I gave a series of talks on multi-scale and locality sensitive representation learning at MIT CSAIL, the Martinos Center, and Brigham and Womens Hospital.
09/22I started a postdoc at MIT CSAIL working with Polina Golland’s group.
09/22Our work on developing multi-scale and locality-sensitive self-supervised representation learning stategies was accepted by NeurIPS 2022!
08/22Our work building transformers for MRI reconstruction was accepted by WACV 2023.
07/22Our work on unsupervised MRI reconstruction was accepted by Medical Image Analysis.
07/22I defended my PhD!
06/22I received a student travel award for MICCAI 2022.
06/22Our work on representation learning for multi-modality deformation was accepted to MICCAI 2022. Preprint here.
05/22I was an outstanding reviewer for CVPR 2022.
04/22Received the Pearl Brownstein Doctoral Research Award from NYU CSE for “doctoral research which shows the greatest promise”.
02/22We had four abstracts on self-supervised image registration, reconstruction, and denoising from my internship work with Hyperfine accepted to ISMRM 2022.
07/21Our paper on learning deformable templates was accepted by ICCV 2021! Head over to our webpage for the preprint, code, and highlights.
06/21Our paper (jointly led by Mengwei Ren and Heejong Kim) was accepted by MICCAI 2021: code/preprint/demos, all here.
05/21I’m back at Hyperfine Research for a summer internship, working on deep learning research applied to low-field (64 mT) MRI.
05/21I gave a (virtual) talk at the VoxelTalk seminar series at MIT/Harvard Medical School.
04/21Our paper (led by Shijie Li) won 3rd place for the best paper award at IEEE ISBI 2021.
02/21Our work (jointly led with Axel Elaldi) on learning Rotation-Equivariant Sparse Spherical Deconvolution was accepted by Information Processing in Medical Imaging (IPMI).
02/21Our work (jointly led with Mengwei Ren) on improving image translation performance and robustness was accepted by IEEE Transactions on Medical Imaging.
01/21My work on Group-Equivariant GANs was accepted by the International Conference on Learning Representations (ICLR) 2021!
01/21Our work led by Shijie Li on point annotation-supervised 2D/3D microscopy segmentation was accepted by IEEE ISBI 2021.
10/20I was an outstanding reviewer for MICCAI 2020.