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.
My research primarily focuses on computer vision applied to medical and biological images - a field that fortunately offers both fun technicality and real-world relevance.
Starting with a heavy interest in low-rank matrix and tensor factorizations early in my Ph.D., I became interested in group equivariant networks, adversarial learning, and geometric deep networks. I’m constantly surprised at the crossover.
- 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. Pre-print available here.
- Aug 2020: Finished an exhilarating 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.
- Nov 2019: Accepted a Summer 2020 internship at Hyperfine Research in NYC.
- Oct 2019: Presented our work on robust tensor decompositions at MICCAI in Shenzhen.