Pau Batlle
PhD Candidate, California Institute of Technology
{firstname}@caltech.edu
I am a PhD candidate in Computing and Mathematical Sciences at Caltech, where I am fortunate to be advised by Houman Owhadi.
Prior to joining Caltech, I graduated from Universitat Politècnica de Catalunya with a double degree in Mathematics and Engineering Physics and I was a research intern at the Center for Data Science at NYU. I have done research at different institutions and companies, which can be found in my résumé
My current research areas include Statistics, Uncertainty Quantification and Gaussian Processes, both from a theoretical point of view and applications. I have worked in different domain applications including particle physics, biology, earthquake prediction, epidemic modelling and telecommunications engineering. I published in journals and conferences across physics, diverse areas of applied mathematics and machine learning, see my publications.
Recent news
My paper Kernel Methods are competitive for Operator Learning has been published in the Journal of Computational Physics (JCP) - November 2023
I passed my candidacy! Find the slides from my candidacy talk here - October 2023
My new preprint about constrained inference in inverse problems, including a disproof of the Burrus conjecture, is now out! Optimization-based frequentist confidence intervals for functionals in constrained inverse problems: Resolving the Burrus conjecture. Slides and a poster are available - October 2023
I gave a talk about my paper on kernel methods for operator learning at ICIAM 2023, in the "Machine Learning in infinite dimensions" minisymposium. Slides are available here - August 2023
I attended the Graduate Student Mathematical Modeling Camp (GSMMC) and the Mathematical Problems in Industry (MPI) Workshop. I worked on chaotic ODE modeling for wind speed prediction during the camp and a Darcy Flow PDE source inversion problem during the workshop, in collaboration with Pacific Northwest National Lab (PNLL) - June 2023
New preprint available: Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs - May 2023
New preprint available: Kernel Methods are competitive for Operator Learning - April 2023
My paper in Multiclass classification utilising an estimated algorithmic probability prior has been accepted for publication in Physica D: Nonlinear phenomena - March 2023