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Achievements

Achievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

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JLaborda/README.md

👋 Hi, I'm Jorge Laborda

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.

🔹 Skills

  • 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,...

👨‍🔬 Work Experience

  • [2019-2025] AI PhD Student Researcher: I was a PhD student doing my thesis about Bayesian Networks
  • [2019-2023] University Professor Associate: During my PhD, I prepared and gave 160 hours of classes regarding Java Programming, Programming Methodology, and Concurrency in Java.

🧑‍🎓 Education

  • [2019-2025] PhD in Artificial Intelligence: PhD in AI from the University of Castilla-La Mancha.
  • [2018-2019] Master's Degree of Research in Artificial Intelligence: MD of Research in AI from the University of Menendez Pelayo. link.
  • [2020-2021] Master's Degree in Data Science and Scalable Applications in the Cloud: MD CIDAEN from the University of Castilla-La Mancha. link.
  • [2014-2018] Bachelor's Degree in Computer Engineering specializing in Computer Science: University of Castilla-La Mancha.

🏆 Achievements

  • Published two Q1 journal papers on Bayesian Networks and Distributed Learning
  • Developed pGES, a novel algorithm for Bayesian Network structure learning
  • Implemented an MCTS-based search algorithm to improve the quality of Bayesian Network structures

🔬 Published Papers

  • [2024] Parallel structural learning of Bayesian networks: Iterative divide and conquer algorithm based on structural fusion. Knowledge-Based Systems. IF: 7.2. Ranking JCR: Q1. link
  • [2024] Distributed fusion-based algorithms for learning high-dimensional Bayesian Networks: Testing ring and star topologies. International Journal of Approximate Reasoning. IF: 3.2. Ranking JCR: Q2. link
  • [2021] Efficient and accurate structural fusion of Bayesian networks. Information Fusion. IF: 17.564. Ranking JCR: Q1. link

📂 Featured Projects

  • 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

📫 Let's connect!

Pinned Loading

  1. cges cges Public

    Circular Greedy Equivalence Search

    Jupyter Notebook 2

  2. ParallelBNs ParallelBNs Public

    Jupyter Notebook 2 1

  3. MCTS-BN MCTS-BN Public

    Forked from ptorrijos99/MCTS-BN

    Monte Carlo Tree Search for Structural Learning of Bayesian Networks

    Java