I'm a PhD in Artificial Intelligence with a strong background in Bayesian Networks, Machine Learning, and Cloud Computing. My PhD thesis focuses on Structural Learning and Fusion of Bayesian Networks, with applications in high-dimensional domains and distributed learning.
- Programming: Python, Java, SQL,...
- Machine Learning & AI: Bayesian Networks, Deep Learning, Reinforcement Learning,...
- Cloud & DevOps: AWS, Docker, MongoDB, CI/CD, Github Actions,...
- Data Science: Pandas, NumPy, Scikit-learn, ETL,...
- AI PhD Student Researcher: I was a PhD student doing my thesis about Bayesian Networks (2019-2025)
- University Professor Associate: During my PhD, I prepared and gave 160 hours of classes regarding Java Programming, Programming Methodology, and Concurrency in Java. (2019-2023)
- PhD in Artificial Intelligence: PhD in AI from the University of Castilla-La Mancha. (2019-2025)
- Master's Degree of Investigation in Artificial Intelligence: MD of Investigation AI from the University of Menendez Pelayo (2018-2019) link.
- Master's Degree in Data Science and Cloud Data Engineering: MD CIDAEN from the University of Castilla-La Mancha. (2020-2021) link.
- Bachelor's Degree in Computer Engineering specializing in Computer Science: University of Castilla-La Mancha. (2014-2018).
- Published three Q1 journal papers on Bayesian Networks and Distributed Learning
- Developed pGES, a novel algorithm for Bayesian Network structure learning
- Implemented an MCTS-based search to improve Bayesian network structures
- pGES Algorithm: A scalable approach to Bayesian Network learning that combines distributed learning and Bayesian Network Fusion. link
- Circular/Ring Greedy Equivalence Search (cGES): A different topological approach to Bayesain Network learning that combines distributed learning and Bayesian Network Fusion. link
- Monte Carlo Tree Search for Bayesian Networks: Enhancing network structure optimization with MCTS. link
- LinkedIn: Jorge Daniel Laborda
- GitHub: @JLaborda