Research

I develop statistical and computational methods for learning and decision-making under uncertainty. My interests span Bayesian inference, probabilistic machine learning, optimization, and decision theory.

I enjoy working across disciplines on exciting applications with practical impact, including molecular and materials design, health, and automated driving. Robustness and adversarial machine learning are also part of my research.

Selected publications

The papers below give a selection of this work. For the full list, see Google Scholar.

• Posterior Attraction with Exponential-Family Likelihoods and Their Conjugate Priors

  William N. Caballero, Roi Naveiro and Brian J. Lunday

  Bayesian AnalysisAdvance publication

• Simulation based Bayesian Optimization

  Roi Naveiro and Becky Tang

  Statistics and Computing2025

  Open preprint

• Evasion Attacks Against Bayesian Predictive Models

  Pablo G. Arce, Roi Naveiro and David Ríos Insua

  Uncertainty in Artificial Intelligence (UAI)2025

• Poisoning Bayesian Inference via Data Deletion and Replication

  Matthieu Carreau, Roi Naveiro and William N. Caballero

  Artificial Intelligence and Statistics (AISTATS)2025

• Adversarial Machine Learning: Bayesian Perspectives

  David Ríos Insua, Roi Naveiro, Víctor Gallego and Jason Poulos

  Journal of the American Statistical Association2023

  Open preprint

• Augmented Probability Simulation Methods for Sequential Games

  Tahir Ekin, Roi Naveiro, David Ríos Insua and Alberto Torres-Barrán

  European Journal of Operational Research2023

• Design of New Dispersants Using Machine Learning and Visual Analytics

  María Jimena Martínez, Roi Naveiro, Axel J. Soto et al.

  Polymers2023