Enrique Fernandez-Blanco

Associate Professor · University of A Coruña · CITIC · RNASA-IMEDIR

enrique-fernandez-blanco-udc.jpg

Faculty of Computer Science

University of A Coruña

A Coruña, Spain

ORCID: 0000-0003-3260-8734

Enrique Fernandez-Blanco is an Associate Professor at the University of A Coruña, affiliated with the Department of Computer Science and Information Technologies, CITIC, and the RNASA-IMEDIR research group.

His work focuses on applied artificial intelligence and the design of data-driven methods that are robust, useful, and empirically grounded.

His main research lines are AI for medicine and health, signal analysis and intelligent sensing, applied AI across real-world domains, and sequential decision-making systems. Across these areas, he combines machine learning, deep learning, evolutionary computation, and reinforcement learning to build models that support prediction, interpretation, and decision-making in complex settings.

A recurrent goal in his work is to connect methodological development with realistic applications, especially in contexts where model performance, reliability, and practical utility matter.

Research interests

  • Applied Artificial Intelligence
  • AI for Medicine and Health
  • Signal Analysis and Intelligent Sensing
  • Sequential Decision-Making Systems
  • Machine Learning, Deep Learning, and Reinforcement Learning

selected publications

  1. P2PFL: A modular framework for Decentralized Federated Learning Selected Q1 JCR
    Pedro Guijas Bravo, Sadi Alawadi, Daniel Rivero Cebrián, and Enrique Fernandez-Blanco
    Array, Dec 2026
    DOI: 10.1016/j.array.2026.101235
  2. Advanced Ocean Modeling and Machine Learning to Forecast Dinophysis Acuminata Blooms: A Tool to Support Shellfish Farming Management Selected D1 JCR
    Elisabet Rocruz, Andres Molares-Ulloa, Xosé A Padin, Rita Nolasco, Daniel Rivero, and 2 more authors
    Ecological Informatics, 2025
    DOI: 10.1016/j.ecoinf.2025.103438
  3. Towards improved harmful algal bloom forecasts: A comparison of symbolic regression with DoME and stream learning performance Selected D1 JCR
    Andres Molares-Ulloa, Elisabet Rocruz, Daniel Rivero, Xosé A Padin, Rita Nolasco, and 2 more authors
    Computers and Electronics in Agriculture, 2025
    DOI: 10.1016/j.compag.2025.110112
  4. Q-learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments Selected Q1 JCR
    Alejandro Puente-Castro, Daniel Rivero, Eurico Pedrosa, Artur Pereira, Nuno Lau, and 1 more author
    Expert Systems with Applications, 2024
    DOI: 10.1016/j.eswa.2023.121240
  5. New machine learning approaches for real-life human activity recognition using smartphone sensor-based data Selected Q1 JCR
    Daniel Garcia-Gonzalez, Daniel Rivero, Enrique Fernandez-Blanco, and Miguel R Luaces
    Knowledge-Based Systems, 2023
    DOI: 10.1016/j.knosys.2023.110260
  6. DoME: A deterministic technique for equation development and Symbolic Regression Selected Q1 JCR
    Daniel Rivero, Enrique Fernandez-Blanco, and Alejandro Pazos
    Expert Systems with Applications, 2022
    DOI: 10.1016/j.eswa.2022.116712
  7. Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques Selected Q1 JCR
    Alejandro Puente-Castro, Enrique Fernandez-Blanco, Alejandro Pazos, and Cristian R Munteanu
    Computers in Biology and Medicine, 2020
    DOI: 10.1016/j.compbiomed.2020.103764
  8. EEG signal processing with separable convolutional neural network for automatic scoring of sleeping stage Selected Q1 JCR
    Enrique Fernandez-Blanco, Daniel Rivero, and Alejandro Pazos
    Neurocomputing, 2020
    DOI: 10.1016/j.neucom.2020.05.085