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We are a group of researchers working on challenging Mathematical Optimization problems using numerical methods based on Mathematical Programming, Machine Learning, and Artificial Intelligence.
We love to put optimization theory into practice while solving industrial applications.
We are passionate about new theoretical questions coming from real-life applications.
RESEARCH TOPICS
RESEARCH TOPICS
Computational Optimal Transport
Computational Optimal Transport
Model and Algorithms for Kantorovich-Wasserstein distances, Wasserstein Barycenters, and Fourier-based metrics
Healthcare Management
Healthcare Management
Models and algorithms to deal with challenges arising from health service management and to support decisions under uncertainty
Combinatorial Optimization
Combinatorial Optimization
Traveling Salesman Problem, Graph Coloring, Stable Set, Total Matching, and Total Coloring
Interpretable Machine Learning
Interpretable Machine Learning
Development of Machine Learning techniques based on interpretable mathematical models that provide explainable outcomes
Multimodal Routing
Multimodal Routing
Joint Project with EU-JRC, Seville: Development of VelociRaptor, a parallel schedule-based multimodal routing solver, customized for all-pairs shortest path queries
Robust Optimal Power Flow
Robust Optimal Power Flow
Development of models and algorithms regarding the Optimal Power Flow problem in a stochastic setting that arises from Renewable Energy Sources
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