José Ángel Martín Baos is an Assistant Professor at the University of Castilla-La Mancha. He obtained his PhD in Advanced Computer Technologies from the same university in 2023, with an International Doctorate mention and summa cum laude distinction.
His work sits at the intersection of machine learning, artificial intelligence, optimisation, and transportation, with a particular focus on behavioural and demand modelling. He also teaches Applied Mathematics in the Computer Engineering degree and coordinates its bilingual programme.
Computational methods for understanding behaviour, modelling demand, and improving decision-making in transportation systems.
Responsibilities include: Research in the field of machine learning and artificial intelligence applied to transportation and discrete choice models. Teaching courses about applied mathematics, artificial intelligence, and machine learning.
This contract is linked to an FPU (Ayudas para la formación de profesorado universitario) grant from the Spanish Ministry of Science, Innovation and Universities. (Ref. FPU18/00802).
Research in the MAT (Modelos y Algoritmos en Sistemas de Transportes) research group under the research proyect 'TRANSPORTE PÚBLICO, CERCANÍAS, AÉREO, RECUPERABILIDAD, ROBUSTEZ, COMPETENCIA, OPTIMIZACIÓN, SIMULACIÓN, MODELIZACION. TRA2016-76914-C3-2-P. (AEI / FEDER, UE)'
Research scholar in the MAT research group and Scientific Computing Group (SciCom) at University of Castilla-La Mancha
University of Castilla-La Mancha
Machine learning methods applied to transport demand modelling Summa cum laude · International Doctorate mention FPU grant from the Spanish Ministry of Science, Innovation and Universities. Research stay at the École polytechnique fédérale de Lausanne (EPFL), June–September 2021. Download dissertationUniversity of Castilla-La Mancha
Awarded as the most outstanding graduate.University of Castilla-La Mancha
Awarded as the most outstanding graduate.I teach mathematics for Computer Engineering, currently including Algebra and Discrete Mathematics and Calculus and Numerical Methods.

Developed optimisation-with-constraint-learning methods for pricing in competitive railway markets, combining the ROBIN simulator with predictive machine-learning models.
Worked with the Behaviour & Infrastructure Group on interpretable neural-network approaches for behavioural modelling and random utility models.
Predoctoral research stay at EPFL, Lausanne, Switzerland.
Methods and technical tools I work with regularly.
Awards recognising research, teaching, and academic achievement.