Biography:
David Domínguez Barbero is a research assistant at the Energy System Modeling area, applying Reinforcement Learning techniques. He born in Toledo, in 1993. He obtained his Bachelor’s Degree (2016) in Computer Engineering in 2016 at the Universidad de Castilla – La Mancha, and his Master’s Degree (2017) in Artificial Intelligence Research with the specialty in reasoning and planning at Menendez Pelayo International University. In addition, he worked at Medsavana S.L. as natural language processing developer in health care.
Skills:
DRL, NLP, DL, ML, data analysis, processing, storing and retrieval. Computer engineer related skills (databases, web aplication design and backend, APIs, software distribution and licenses, good design patterns). Python language (4-5 years of experience) with libraries mainly ML and visualization related (2-3 years of experience). General algorithm design and optimization (tree search, graph search, combinatorial optimization, linear optimization, dynamic programming, meta-heuristics, planning) and optimization advanced tools (data structures, parallelization and concurrence, profiling, debugging). Knowledge in statistics and linear algebra mainly, but also game theory, graph theory and others. LaTeX, Linux, Git.
Domínguez-Barbero, D., García-González, J., Sanz-Bobi, M.A., García-Cerrada, A., Energy management of a microgrid considering nonlinear losses in batteries through Deep Reinforcement Learning. Applied Energy. Vol. 368, pp. 123435-1 - 123435-12, August 2024. [Online: May 2024] 
JCR: 10,100 Q1 (2023) - SJR: 2,820 Q1 (2023) Domínguez-Barbero, D., García-González, J., Sanz-Bobi, M.A., Twin-delayed deep deterministic policy gradient algorithm for the energy management of microgrids. Engineering Applications of Artificial Intelligence. Vol. 125, pp. 106693-1 - 106693-11, October 2023. [Online: July 2023]
JCR: 7,500 Q1 (2023); - SJR: 1,749 Q1 (2023); Domínguez-Barbero, D., García-González, J., Sanz-Bobi, M.A., Sánchez-Úbeda, E.F., Optimising a microgrid system by deep reinforcement learning techniques. Energies. Vol. 13, nº. 11, pp. 2830-1 - 2830-19, June 2020. [Online: June 2020]
JCR: 3,004 Q3 (2020); 3,000 Q3 (2023) - SJR: 0,598 Q2 (2020); 0,651 Q1 (2023) 
