I am an instructor in Computing + Mathematical Sciences at Caltech hosted by Andrew Stuart and Houman Owhadi. My research is on probabilistic modeling and inference for problems in science and engineering. Most recently, I have been developing scalable generative models for solving inverse problems based on computational measure transport.
I completed my PhD in the Center for Computational Science and Engineering at MIT where I was fortunate to be advised by Youssef Marzouk. A copy of my PhD thesis can be found here. Before MIT, I received my BASc in Engineering Science from the University of Toronto.
|Gave the USNCCM Large-Scale TTA early-career colloquium on dimension reduction methods for probabilistic modeling. Thank you Shelly and Patrick for the invitation!
|Our paper on learning monotone triangular transports was accepted for publication in Foundations of Computational Mathematics!
|Our two submissions to NeurIPS 2023: Debias Coarsely, Sample Conditionally for Statistical Downscaling (spotlight) and Structured Neural Networks (poster) were accepted! Many thanks to my co-authors for the great collaborations.
- SIAM ReviewCoupling techniques for nonlinear ensemble filteringSIAM Review, 2022
- FoCMOn the representation and learning of monotone triangular transport mapsFoundations of Computational Mathematics, 2023
- arXivConditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free InferencearXiv:2006.06755, 2023