Announcement of the Spanish Society of Statistics, Operations Research and Data Science – BBVA Foundation Awards 2026

The committee of the Spanish Society of Statistics, Operations Research and Data Science (SEIO) – BBVA Foundation Awards 2026, meeting online on July 6th, 2026, and ratified by the SEIO Executive Board on July 8th, 2026, has decided to award the following prizes:

Best methodological contribution in Statistics

Eustasio del Barrio, Professor of Statistics and Operations Research at the Universidad de Valladolid (Spain); Alberto González Sanz, Assistant Professor in the Department of Statistics at Columbia University (United States); and Marc Hallin, Professor Emeritus of Mathematics at the Université libre de Bruxelles (Belgium), for their article Nonparametric Multiple-Output Center-Outward Quantile Regression, published in the Journal of the American Statistical Association.

Classical quantile regression is a central tool for describing conditional distributions, but its extension to multiple-output responses has long been obstructed by the lack of a satisfactory multivariate notion of quantiles.  Based on recent results on measure transportation, this paper provides a very elegant and a neat solution to this problem.  The contribution is also timely because optimal transport has become one of the most active mathematical tools in modern statistics. This paper brings that programme to nonparametric multiple-output regression, with direct implications for multivariate forecasting, risk analysis, value-at-risk and expected shortfall, environmental and biomedical multi-response modelling, and machine-learning prediction regions with joint rather than marginal coverage. The paper is mathematically very solid and is of high practical relevance.

Best methodological contribution in Operations Research

Juan Miguel Morales González, Professor of Statistics and Operations Research at the Universidad de Málaga (Spain), and Adrián Esteban Pérez, Associate Senior Lecturer in the Department of Computer and Systems Sciences at Stockholm University (Sweden), for their article Distributionally Robust Stochastic Programs with Side Information Based on Trimmings, published in Mathematical Programming.

This paper is devoted to data-driven stochastic optimization. The authors introduce a distributionally robust optimization framework that incorporates side information in addition to the data on the uncertainties. By constructing ambiguity sets based on probability trimmings, they obtain robust decisions under limited or contaminated data while preserving computational tractability and performance guarantees.

Best applied contribution in Statistics

Lucas Fernández-Piana, Assistant Professor in the Department of Mathematics and Sciences at the Universidad de San Andrés (Buenos Aires, Argentina); Ana Justel, Professor of Statistics at the Universidad Autónoma de Madrid (Spain); and Marcela Svarc, Associate Professor in the Department of Mathematics at the Universidad de San Andrés (Buenos Aires, Argentina), for their article Integrated Depth for Trajectories of Airborne Microorganisms to Antarctica, published in The Annals of Applied Statistics.

This article constitutes a highly innovative contribution to applied statistics, advancing both methodological foundations and practical analysis of complex functional data. The main contribution is a new depth framework designed for trajectory bouquets, collections of curves sharing an origin or endpoint and spreading in multiple directions. This data structure appears frequently in modern applications: atmospheric transport, ocean circulation, movement ecology, and land-based dispersion. Consequently, since its publication it has received a considerable attention, also reflected in its high number of citations proving its important impact on its field.

Best applied contribution in Operations Research

Sergio Cavero, Assistant Professor of Computer Science and Artificial Intelligence at Universidad Rey Juan Carlos (Spain); Manuel Laguna, Professor of Management Science at the University of Colorado Boulder (United States); and Eduardo G. Pardo, Professor of Computer Science and Artificial Intelligence at Universidad Rey Juan Carlos (Spain), for their article Solving a Short Sea Inventory Routing Problem in the Oil Industry, published in Computers & Industrial Engineering.

This contribution develops an optimization methodology for a complex inventory routing problem in the oil industry, combining methodological rigor with clear industrial relevance. The proposed mixed-integer optimization model has been successfully implemented in a real industrial environment, leading to significant reductions in distribution costs, operational costs, and the number of voyages. The work illustrates the value of Operations Research in addressing complex industrial problems through rigorous methodology and successful technology transfer.

Best contribution in Statistics and Operations Research applied to Data Science

David Ríos Insua, Research Professor at the Institute of Mathematical Sciences (ICMAT-CSIC, Spain); Roi Naveiro, Tenure-Track Assistant Professor in the Department of Quantitative Methods at CUNEF Universidad (Spain); Víctor Gallego, Associate Professor at IE University (Spain); and Jason Poulos, Chief Research Officer at Komorebi AI (Spain), for their article Adversarial Machine Learning: Bayesian Perspectives, published in the Journal of the American Statistical Association.

Machine Learning (AML), a field of major importance in security and cybersecurity for protecting systems that increasingly rely on ML algorithms. Existing work has framed most of this research within game theory, entailing common knowledge (CK) conditions that rarely hold in real AML security contexts. To address this limitation, a Bayesian framework is proposed as an alternative to game-theoretic AML, explicitly modelling uncertainty about the opponent’s beliefs and interests and relaxing the unrealistic CK assumptions that undermine existing approaches. The resulting models are empirically shown to be more robust to deviations in attacker behaviour, better reflecting the uncertainties inherent in adversarial settings. This constitutes the first comprehensive and formally grounded Bayesian approach to AML, with direct relevance to the development of safe and secure artificial intelligence systems.

Committee

The international committee has a membership proposed by SEIO and the BBVA Foundation. Chairing the committee on this occasion was Albert Satorra, Professor Emeritus of Statistics at Universitat de Pompeu Fabra and Research Professor at the Barcelona School of Economics. Its members were: Ana Paula Barbosa-Póvoa, Professor of Operations Research and Logistics at the Instituto Superior Técnico (IST) of the University of Lisbon (Portugal); Concha Bielza, Professor of Statistics and Operations Research at the Universidad Politécnica de Madrid (Spain); Dolores Romero, Professor of Operations Research at Copenhagen Business School (Denmark); Stefan Sperlich, Director of the Research Institute for Statistics and Information Science at the University of Geneva (Switzerland); and Jane-Ling Wang, Distinguished Research Professor in the Department of Statistics at the University of California, Davis (USA).