Oscar Leong

### von Kármán Instructor

### Department of Computing + Mathematical Sciences

### California Institute of Technology

### About me

I am a von Kármán Instructor at Caltech in the Computing + Mathematical Sciences department, hosted by Venkat Chandrasekaran. I also work with Katie Bouman and the Computational Cameras group. I will be joining UCLA Statistics and Data Science as a tenure-track Assistant Professor in July 2024. I completed my PhD from Rice University in Computational and Applied Mathematics under the supervision of Paul Hand and was an NSF Graduate Research Fellow. I received my undergraduate degree in Mathematics from Swarthmore College.

My research interests lie in the mathematics of data science, inverse problems, machine learning, and optimization. Much of my work concerns solving signal recovery problems with approaches inspired by deep learning and uses tools from high dimensional probability, random matrix theory, and optimization to develop provable recovery guarantees.

Family: my wife, Wani, is in developmental psychology. We welcomed our first daughter, Gemma, in May of 2023.

### News

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.

### Talks

Here is a video of my oral presentation at NeurIPS 2018 on our Deep Phase Retrieval paper along with a link to a 3 minute summary of our work: