Ceci n’est pas une bio
PhD candidate at Carlos III University of Madrid (Bayesian inference, measure-theoretic ML). Former Devo (Loxcope) software engineer and ESO data engineer; CERN Openlab alumnus. Interests: computational statistics, deep learning, dynamical systems, scientific computing. Stack: Julia/Python, plus Java/SQL/JS; Docker, Kubernetes, Spark, Git, CI/CD.
Research
I study the probabilistic and theoretical foundations of machine learning, especially Bayesian inference and measure-theoretic statistics. My current work develops new loss functions for deep learning, and extends to rank-based methods and sliced divergences with links to optimal transport.
News
Paper accepted at ICML 2026
Our paper Approximating f-Divergences with Rank Statistics, joint work with Viktor Stein, has been accepted at ICML 2026!
Paper accepted in JMLR
Paper accepted in the Journal of Machine Learning Research (March 2026)! Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss
Paper accepted at AISTATS 2026
Accepted at AISTATS 2026 as a Spotlight presentation (January 2026)! Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence. 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