Ceci n’est pas une bio

I’m currently pursuing a PhD at Carlos III University of Madrid, where I investigate the probabilistic and theoretical foundations of AI and machine learning, with a particular emphasis on Bayesian inference and measure‑theoretic statistics. Before academia, I worked as a software engineer on the Loxcope team at Devo, and served as a data engineer at the European Southern Observatory, as well as participating in the CERN Openlab program. My interests span computational statistics, deep learning, geometric measure theory, dynamical systems, scientific computing, and software engineering. I’m proficient in Julia, Python, Java, SQL, and JavaScript, and have hands‑on experience with Docker, ANTLR4, Kubernetes, Apache Spark, Git, and CI/CD pipelines.

Research

My work centers on the probabilistic and theoretical foundations of AI and machine learning, with a particular emphasis on Bayesian inference and measure‑theoretic statistics.
Currently, I am developing novel loss functions to enhance the training of deep neural networks. I am also expanding my research into rank statistics and their connections with optimal transport theory.

News

New article submitted to Pattern Recognition

New article submitted to Pattern Recognition (November 2024)! Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss. Jose Manuel de Frutos, Pablo M. Olmos, Manuel Alberto Vázquez, Joaquín Míguez

Paper accepted at AISTATS 2024

Paper accepted at AISTATS 2024 (January 2024)! Training Implicit Generative Models via an Invariant Statistical Loss. Jose Manuel de Frutos, Pablo M. Olmos, Manuel Alberto Vázquez, Joaquín Míguez