November 2023: I gave a talk in the Math Machine Learning seminar, joint with MPI MiS and UCLA, on generative networks for inverse problems.
September 2023: Our paper on solving inverse problems without explicit priors by exploiting common structure has been accepted to IEEE Transactions on Computational Imaging!
February 2023: Our paper on establishing optimal sample complexity in phase retrieval with deep generative priors accepted to Communications on Pure and Applied Mathematics!
February 2023: Two papers accepted to ICASSP 2023, one on Rohun's SURF project and the other with Angela, He, and Katie on image reconstruction without explicit priors!
December 2022: New preprint out with Eliza, Yong Sheng, and Venkat on characterizing optimal regularizers for a data distribution!
December 2022: Honored to have been awarded an MGB-SIAM Early Career Fellowship.
November 2022: Rohun's SURF project on denoising priors for phase retrieval has been uploaded to arXiv as a preprint!
October 2022: Honored to have been selected as a Rising Star in Data Science by UChicago.
July 2022: I gave a talk at ICCOPT on new work with Yong Sheng and Venkat on our variational analysis of learning convex regularizers. A preprint will be posted soon!
May 2022: I presented during the CMX student/postdoc seminar on some new, exciting work with Angela, He, and Katie on learning image models directly from noisy data! Please see our webpage for more info.
February 2022: Mateo Díaz, Yong Sheng Soh, and I are organizing a pair of sessions on "Convex and nonconvex methods for matrix factorization problems" at ICCOPT 2022!
December 2021: I will be serving as a local arrangement chair for ICCP 2022!
October 2021: Will be presenting in-person (first time in awhile!) during Caltech's CMI seminar on using deep generative models in inverse problems.
September 2021: I'll be virtually presenting our generative prior work at the "Generative Regularization Approaches for Inverse Problems" Minisymposium at the IFIP TC7 Conference.
May 2021: Our paper on analyzing subgradient descent for phase retrieval using non-Lipschitz matrix concentration accepted to Communications in Mathematical Sciences.
April 2021: I successfully defended my dissertation! Excited to announce I'll be joining Caltech in the Department of Computing + Mathematical Sciences as a von Kármán Instructor in the Fall.