Profesores e Investigadores

Curriculum Vitae

Quinquenios

2020

Docentia

2023-24

Sexenios investigación

2021

Centro

E. Ingeniería de Fuenlabrada

Departamento

Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación

Área

Teoría de la Señal y Comunicaciones

Grupos de investigación

Grupo de investigación consolidado en Aprendizaje de Sistemas Dinámicos (Learning Dynamical Systems)
Grupo de investigación de alto rendimiento en Ingeniería de Microondas y Sistemas de Radiocomunicación / Microwave Engineering and Radiocommunication Systems

Centro/Instituto de investigación

Centro de Investigación de Data, Complex Networks & Cybersecurity Sciences de la Universidad Rey Juan Carlos

Contacto

leopoldo.carro@urjc.es

Presentación

Leopoldo Carro Calvo is an interim Associate Professor and Coordinator of the University's Degree in Telecommunication Engineering at the School of Engineering in Fuenlabrada. He obtained a Doctorate in Information and Communication Technologies from the University of Alcalá in 2013.

Starting his doctoral thesis, Leopoldo spent three months at Justus Liebig University Giessen, Germany, working on a project for MedCLIVAR (Mediterranean CLImate VARiability and predictability) of the European Science Foundation (ESF). He was also funded by the Iberdrola Foundation Scholarships "Ayudas a la Investigación en Energía y Medio Ambiente" in 2012 and 2013. Following his thesis, he continued working as a Research and Teaching Staff Member at the University of Alcalá, focusing on estimation techniques associated with wind energy. Additionally, he collaborated on the development of optimization methods based on surrogate models within the GARTEUR AD/AG52 (Group for Aeronautical Research and Technology in Europe, Action Group 52).

In June 2015, he became a researcher at the Institute of Meteorology and Climate Research at the Karlsruhe Institute of Technology (KIT), Germany. During this time, he applied soft-computing techniques to problems related to cloud feature analysis using remote sensing measurements. He continued his research at the Department of Earth Physics, Astronomy, and Astrophysics II at the Complutense University of Madrid (UCM) from June 2016 to May 2018, where he developed atmospheric pattern analysis techniques under a "Juan de la Cierva - Formación" contract.

He joined Rey Juan Carlos University as an Assistant Professor in the Department of Signal Theory and Communications and Telematic and Computing Systems in the academic year 2018/2019. Since then, he has been actively involved in teaching a diverse range of University Degree programs, including Telecommunication Engineering, Biomedical Engineering, Audiovisual and Multimedia Systems Engineering, and Aerospace Engineering with a specialization in Aeronavigation. He became an interim Associate Professor in November 2021 and the coordinator of the degree in Telecommunications Technologies at the university in October 2022.

Leopoldo has made significant contributions in machine learning, soft computing, and optimization algorithms. His expertise has been applied to tackle complex problems in various fields including telecommunications, aerospace engineering, renewable energies, the environment, and climate change. His work in signal theory and communications has advanced understanding in these areas.

He has authored over 20 scientific articles published in reputable journals. One notable publication is the paper titled "Deep Neural Networks: an Alternative to Traditional Channel Estimators in Massive MIMO Systems" in the IEEE Transactions on Cognitive Communications and Networking 2022, co-authored with Antonio Melgar González, Alejandro de la Fuente, Óscar Barquero Pérez, and Eduardo Morgado Reyes. Another notable publication is the paper "k-Gaps: a novel technique for clustering incomplete climatological time series" in Theoretical and Applied Climatology in 2020, co-authored with Fernando Jaume Santero, Ricardo García Herrera, and Sancho Salcedo Sanz.

Leopoldo's research also covers temperature field reconstruction, aerodynamic shape design, total column ozone prediction, and wind farm optimization. His multidisciplinary approach and application of machine learning and optimization algorithms are evident in these diverse research areas.

In addition to his extensive publication record, he has been involved in several research projects and contracts. These projects highlight his active involvement in collaborative research and his ability to secure funding to support his research endeavors.