About me

I’m a Ph.D. candidate at New York University, advised by Guido Gerig. I’m part of the multi-disciplinary Visualization, Imaging, and Data Analysis (VIDA) Center at NYU which provides a wonderful environment for both research and not losing your mind in gradschool.

Research Interests

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, and deformable registration.

Recent Updates

• May 2022: I was an outstanding reviewer for CVPR 2022.
• April 2022: Received the Pearl Brownstein Doctoral Research Award from NYU CSE for “doctoral research which shows the greatest promise” (equivalent to best departmental Ph.D. thesis).
• Feb 2022: We had four abstracts on self-supervised image registration, reconstruction, and denoising from my internship work with Hyperfine accepted to ISMRM 2022.
• Jan 2022: I just proposed my thesis, which means I’ll be defending it in a few months 🤞
• July 2021: Our paper on learning deformable templates was accepted by ICCV 2021! Head over to our webpage for the preprint, code, and highlights.
• July 2021: I was one of ten outstanding reviewers for MICCAI 2021.
• June 2021: Our paper (jointly led by Mengwei Ren and Heejong Kim) was accepted by MICCAI 2021: code/preprint/demos, all here.
• May 2021: I’m back at Hyperfine Research for a summer internship, working on deep learning research applied to low-field (64 mT) MRI.
• May 2021: I gave a (virtual) talk at the VoxelTalk seminar series at MIT/Harvard Medical School. Recording should be up soon!
• April 2021: Our paper (led by Shijie Li) won 3rd place for the best paper award at IEEE ISBI 2021.
• Feb 2021: Our work (jointly led with Axel Elaldi) on learning Rotation-Equivariant Sparse Spherical Deconvolution was accepted by Information Processing in Medical Imaging (IPMI).
• Feb 2021: Our work (jointly led with Mengwei Ren) on improving image translation performance and robustness was accepted by IEEE Transactions on Medical Imaging.
• Jan 2021: My work on Group-Equivariant GANs was accepted by the International Conference on Learning Representations (ICLR) 2021!
• Jan 2021: Our work led by Shijie Li on point annotation-supervised 2D/3D microscopy segmentation was accepted by IEEE ISBI 2021.
• Oct 2020: I was an outstanding reviewer for MICCAI 2020.
• Aug 2020: Our paper on self-supervised denoising with diffeomorphic templates was accepted at the 2020 MICCAI OMIA workshop.
• Aug 2020: Finished an exciting internship at Hyperfine Research working on inverse problems and deep learning research for low-field (64 mT) MRI.
• May 2020: My work introducing symmetry priors to GANs via group-equivariant networks is now out on arXiv! (phew.)
• April 2020: Our abstract led by Guillaume Gisbert on iterated template building and self-supervised denoising of OCT images was accepted by ARVO Imaging. Teaser results here.
• Oct 2019: Presented our work on robust tensor decompositions at MICCAI in Shenzhen.