# Pau Batlle

### PhD Candidate, California Institute of Technology

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