Jose Ignacio Hidalgo
Universidad Complutense de Madrid
Madrid, Spain
Full Professor (Catedrático Universitario)
Bio
José Ignacio Hidalgo is a Full Professor at Universidad Complutense de Madrid (UCM) with 1,794 citations. He works in the Department of Computer Architecture and Automation at the Faculty of Informatics. He earned his doctorate from UCM in 2001 with a thesis on “Partitioning and placement techniques for multi-FPGA systems based on genetic algorithms.”
His research demonstrates a strong integration of evolutionary computation techniques with practical applications in computer architecture, medical technology, and energy optimization. He leads the research group on Architecture and Technology of Computing Systems at UCM.
Research Focus
Evolutionary Computation: His work investigates the evolutionary process of Grammar-Guided Genetic Programming (GGGP) methods, including Context-Free Grammars GP (CFG-GP), Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE). He has developed methods for accelerating the evaluation of individuals in Grammatical Evolution.
Computer Architecture Applications: His research includes hybridizing evolutionary computation and reinforcement learning for designing autonomous robot controllers, combining the optimization power of evolutionary algorithms with the efficiency of reinforcement learning. He has worked on hybrid evolutionary algorithms for multi-FPGA systems design.
Biomedical Applications: Professor Hidalgo has extensive research in artificial pancreas systems for type 1 diabetes, where algorithms forecast future glucose levels based on food ingestion and insulin bolus sizes. His team has developed methodologies for modeling glucose levels in diabetic patients using Grammatical Evolution, achieving up to 300x reductions in execution time.
Energy and Optimization: His work includes estimating total energy demand in countries from macro-economic variables using meta-heuristic approaches. He has published on leakage and temperature aware server control for improving energy efficiency in data centers.
Education
- PhD in Computer Science - Universidad Complutense de Madrid (2001)
- Thesis: “Técnicas de partición y ubicación para sistemas multi-FPGA basadas en algoritmos genéticos”
Leadership & Research
- Research Group Leader - Architecture and Technology of Computing Systems, UCM
- Department - Computer Architecture and Automation, Faculty of Informatics
Key Research Applications
- Artificial Pancreas Systems - Glucose forecasting for type 1 diabetes management
- Multi-FPGA Systems - Evolutionary optimization for hardware design
- Autonomous Robotics - Hybrid evolutionary-reinforcement learning controllers
- Energy Efficiency - Data center optimization and national energy demand estimation
Interests
Evolutionary Computation | Computer Architecture | Grammatical Evolution | Biomedical Systems | Energy Optimization
