Byron Jaramillo

Contact

Email byron.jaramillovinueza(at)unifr.ch
ORCID 0000-0002-8587-9426
GitHub bojarami83
LinkedIn byronjaramillo
Location Quito, Ecuador

Byron Jaramillo

PhD Student in Computer Science

Research interests

I work at the intersection of machine learning, deep learning, large language models and fuzzy logic, with one common goal: building AI systems whose decisions people can understand and trust. My doctoral research applies these methods to medicine and health, where an explanation matters as much as an accurate prediction. Alongside my research, I work as a data analytics consultant applying machine learning to credit risk, which keeps my work grounded in real, imbalanced and high-stakes data.

  • Explainable AI
  • Fuzzy Inference Systems
  • Fuzzy Rule Extraction
  • Machine Learning for Health
  • Large Language Models
  • Recommender Systems

Projects

  • Interpretable credit scoring with fuzzy inference systems extracted from gradient boosting models, evaluated on public lending data.
  • Explainable recommendation for health decision support, combining machine learning with human-readable linguistic rules.

Teaching

  • Practical Applications of Artificial Intelligence, Escuela Politécnica Nacional (EPN)
  • Universidad San Francisco de Quito (USFQ)
  • Universidad de Las Américas (UDLA)

Publications

2025

  • B. Jaramillo, E. Loza-Aguirre and L. Terán, "Beyond Black Boxes: Implementing Recommender Systems That Generate Trust for Decision-Making in Sensitive Business Areas," 2025 Eleventh International Conference on eDemocracy & eGovernment (ICEDEG), 2025, doi: 10.1109/ICEDEG65568.2025.11081623.

2023

  • B. Jaramillo, E. Loza-Aguirre and L. Terán, "Designing a Framework for Explainable Health Recommender System Based on the Ecuadorian Data Protection Regulations," 2023 Ninth International Conference on eDemocracy & eGovernment (ICEDEG), 2023, doi: 10.1109/ICEDEG58167.2023.10122066.

2017

  • M. Hallo, B. Jaramillo, J. Aguilar, H. Lozada and E. Camargo, "Developing a Mathematical Model of Oil Production in a Well That Uses an Electric Submersible Pumping System," Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vol. 1, 2017, pp. 230-237, doi: 10.5220/0006421202300237.