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.
Areas of interest:
Reinforcement learning, natural language processing, deep learning, and other techniques of modeling, optimization, reasoning, planning and prediction. Quantum computing.
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. 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: 8,000 Q1 (2022) - SJR: 1,729 Q1 (2022) 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); 3,200 Q3 (2022) - SJR: 0,598 Q2 (2020); 0,632 Q1 (2022) 
