Biography

José Ángel Martín Baos is a predoctoral researcher in computer science in the Escuela Superior de Informática of the University of Castilla-La Mancha. There, he graduated with honors on both a BSc. in computer science in the year 2018, and a MSc. in computer science in the year 2019.

He is currently studying his PhD at the University of Castilla-La Mancha as a teaching assistant. Previously, he has been working with a FPU (Ayudas para la formación de profesorado universitario) grant from the Spanish Ministry of Science, Innovation and Universities. He is also working as research assistant in the MAT (Modelos y Algoritmos en Sistemas de Transportes) research group since 2016.

Interests
  • Machine Learning
  • Artificial Intelligence
  • Optimization
  • Discrete Choice Models
Education

  • PhD in Computer Science, 2019-Today

    University of Castilla-La Mancha

  • MSc in Computer Science, 2019

    University of Castilla-La Mancha

  • BSc in Computer Science, 2018

    University of Castilla-La Mancha

Hello, my name is José Ángel

I’m a researcher at the University of Castilla-La Mancha 🏫

About me

Most recent publications

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Recent Posts

A webpage to monitor the propagation of COVID-19 disease
The MAT research group from the University of Castilla-La Mancha is developing a website to visualise and monitor the progress of COVID-19 disease in each country and autonomous community in the case of Spain.
Last updated on Jan 9, 2023 2 min read
A webpage to monitor the propagation of COVID-19 disease
Sysorm 2019
This week I have attended the 2nd Spanish Young Statisticians and Operational Researchers Meeting (SYSORM) where I have presented a platform for monitoring air pollution an road traffic in cities.
Last updated on Jan 9, 2023 1 min read
Sysorm 2019

Most recent publications

A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
Traditionally, Random Utility Maximization (RUM) models have been widely applied to travel mode choice modelling. Currently, Machine …
DOI URL
A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
IoT based monitoring of air quality and traffic using regression analysis
Dynamic traffic management (DTM) systems are used to reduce the negative externalities of traffic congestion, such as air pollution in …
PDF DOI URL
IoT based monitoring of air quality and traffic using regression analysis
A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression
In the last few years, the success of Machine Learning (ML) algorithms has led to the extension of their applications to areas such as …
DOI URL
A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression
Revisiting kernel logistic regression under the random utility models perspective. An interpretable machine-learning approach
The success of machine-learning methods is spreading their use to many different fields. This paper analyses one of these methods, the …
PDF DOI
Revisiting kernel logistic regression under the random utility models perspective. An interpretable machine-learning approach
A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs
Several common general purpose optimization algorithms are compared for finding A- and D-optimal designs for different types of …
DOI URL
A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs
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Recent & Upcoming Talks

Projects

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All Transport
Meeting Artificial Intelligence and Machine Learning for Rail Passenger Service Planning under Competition

Meeting Artificial Intelligence and Machine Learning for Rail Passenger Service Planning under Competition

PID2020-112967GB-C32

Project page
Robustez, eficiencia y recuperación de sistemas de transporte público

Robustez, eficiencia y recuperación de sistemas de transporte público

TRA2016-76914-C3

Project page

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