The NEUROTEC Community gathers researchers from different fields and disciplines focused on the study of the brain. “NEUROTEC Interview Series” will explore the work and careers of prominent members of the community.
Concha Bielza
Concha Bielza received the M.S. degree in Mathematics from Universidad Complutense de Madrid, Madrid, Spain, in 1989 and the Ph.D. degree in Computer Science from Universidad Politécnica de Madrid, Madrid, in 1996 (extraordinary doctorate award). She is currently (since 2010) a Full Professor of Statistics and Operations Research with the Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid.
Her research interests are primarily in the areas of probabilistic graphical models, decision analysis, metaheuristics for optimization, data mining, classification models, and real applications, like biomedicine, bioinformatics, neuroscience, industry and sport analytics. She has published more than 150 papers in impact factor journals and has supervised 22 PhD theses. She was awarded the 2014 UPM Research Prize and the 2020 Research Award for Significant Contribution in the Field of Machine Learning (Amity University, India)
2024
- Blasco T, Balzerani F, Valcarcel L, Larrañaga P, Bielza C, Francino M, Rufián-Henares J, Planes F, Pérez-Burillo S (2024). BN-BacArena: Bayesian network extension of bacArena for the dynamic simulation of microbial communities. Bioinformatics, in press.
- Puerto-Santana C, Larrañaga P, Bielza C (2024). Feature saliencies in asymmetric hidden Markov models. IEEE Transactions on Neural Networks and Learning Systems, 35, 3, 3586-3600.
2023
- Bernaola N, Michiels M,Larrañaga P, Bielza C (2023). Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks. PLOS Computational Biology, 19(12): e1011443.
- Larrañaga P, Bielza C (2023). Estimation of Distribution Algorithms in Machine Learning: A Survey. IEEE Transactions on Evolutionary Computation, in press.
- Puerto-Santana C, Larrañaga P, Bielza C (2023). Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection. Neurocomputing, 554, 126641.
- Soloviev VP, Bielza C, Larrañaga P (2023). Quantum approximate optimization algorithm for Bayesian network structure learning. Quantum Information Processing, 22:19, 1-28.
- Soloviev VP, Bielza C, Larrañaga P (2023). Semiparametric estimation of distribution algorithms for continuous optimization. IEEE Transactions on Evolutionary Computation, in press.
- Valero-Leal E, Bielza C, Larrañaga P, Renooij S (2023). Efficient search for relevance explanations using MAP-independence in Bayesian networks. International Journal of Approximate Reasoning, 160, 108965.
- Valverde G, Quesada D, Larrañaga P, Bielza C (2023). Causal reinforcement learning based on Bayesian networks applied to industrial settings. Engineering Applications of Artificial Intelligence, 125, 106657.
- Villa-Blanco C, Bielza C, Larrañaga P (2023). Feature subset selection for data and feature streams: A review. Artificial Intelligence Review, 56, 1011-1062
- Villa-Blanco C, Bregoli A, Bielza C, Larrañaga P, Stella F (2023). Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers. International Journal of Approximate Reasoning, 159, 108945.
