Experience:
• Complutense University of Madrid, Associate Professor, 10/2020 - Present:
– Teaching courses in Computer Science, Pharmacy, Mathematics, and Statistics
programs.
– Conducting research projects in the field of data science and artificial intelligence.
• Datais (spin-off of Visualitics), Machine Learning Lead, 05/2020 - Present:
– Training, development, and mentoring of projects related to the development
and implementation of causal models and machine learning solutions in the
IoT, engineering, and industrial domains.
• Polytechnic University of Madrid, Researcher, 02/2019 - Present:
– Participation in research projects related to Bayesian networks, probabilistic
graphical models, deep learning, functional statistics, and engineering projects.
• Colegio Universitario de Estudios Financieros - CUNEF (UCM), Associate
Professor, 09/2016 - Present:
– Teaching courses in the Master’s program in Data Science for Finance and
Executive Program in Data Science.
Valverde, G., Quesada, D., Larrañaga, P., Bielza, C., Causal reinforcement learning based on Bayesian networks applied to industrial settings. Engineering Applications of Artificial Intelligence. Vol. 125, pp. 106657-1 - 106657-17, October 2023. [Online: July 2023] 
JCR: 7,500 Q1 (2023); - SJR: 1,749 Q1 (2023);
JCR: 7,802 Q1 (2021); 7,500 Q1 (2023) - SJR: 1,734 Q1 (2021); 1,749 Q1 (2023) Valverde, G., Mira McWilliams, J.M., González-Pérez, B., One-layer vs. two-layer som in the context of outlier identification: a simulation study. Applied Sciences. Vol. 11, nº. 14, pp. 6241-1 - 6241-22, July 2021. [Online: July 2021]
JCR: 2,838 Q2 (2021); 2,500 Q1 (2023) - SJR: 0,507 Q2 (2021); 0,508 Q2 (2023) González-Pérez, B., Núñez, C., Sánchez, J.L., Valverde, G., Velasco, J.M., Expert system to model and forecast time series of epidemiological counts with applications to COVID-19. Mathematics. Vol. 9, nº. 13, pp. 1485-1 - 1485-34, July 2021. [Online: June 2021]
JCR: 2,592 Q1 (2021); 2,300 Q1 (2023) - SJR: 0,538 Q2 (2021); 0,475 Q2 (2023) 
