Carlos Mora Sardiña

I am a PhD candidate at the University of California, Irvine, specializing in computational science and engineering. My research interests lie within the fields of machine learning, data fusion and uncertainty quantification. In my work, I develop probabilistic machine learning methods to model complex systems across various scientific and engineering domains. You can find my publications here. Additionally, I am grateful to have been awarded with the Balsells fellowship.

Prior to my PhD, I graduated in aerospace engineering with honors from the Polytechnic University of Catalonia.

Recent news

August 2025: SEEK received a Paper with Distinction at IDETC-CIE 2025!
June 2025: I started a machine learning engineering internship at Patreon!
March 2025: released a new preprint on SEEK, a novel framework for nonstationary kernel learning in Gaussian Processes.
December 2024: I successfully defended my candidacy exam! My PhD thesis, titled Integrating Deep Learning with Gaussian Processes for Scientific Computing, will explore innovative ways to combine these machine learning methods for advancing scientific computing.
November 2024: my paper Operator learning with Gaussian processes is now available in Computer Methods in Applied Mechanics and Engineering.
October 2024: my paper A gaussian process framework for solving forward and inverse problems involving nonlinear partial differential equations has been published in Computational Mechanics.
October 2024: I gave a talk at the CRUNCH seminar on neural operators and Gaussian Processes for operator learning.
September 2024: new preprint available on a general framework for operator learning via Gaussian Processes.
August 2024: new preprint available where we develop a meshfree topology optimization method with physics-informed Gaussian Processes.