2026
Best Doctoral Thesis Award
Ignacio Ellacuría Award · University of Deusto.
AI & optimization researcher · European R&D
ErickRodríguez-Esparza
From intelligent algorithms to real-world applications.
I am a researcher in artificial intelligence and optimization, developing computational methods and research software for complex decision-making problems. My work bridges scientific research and practical applications across mobility, logistics, and other engineering domains.
Bilbao · Spain
Research snapshot
Research in intelligent optimization, from algorithms to applications.
I connect evolutionary computation and metaheuristics with artificial intelligence, simulation-based optimization, and research software for applied decision support.
My work moves between methodological research and implementation: framing the problem, designing algorithms, developing the software, and evaluating alternatives with domain specialists.
The applications change across projects, but the aim is consistent: build computational methods that can be examined, reproduced, and used in real settings.
More about my research →680Citations
9h-index
9Journal articles
3Horizon Europe projects
Google Scholar metrics as of 28 September 2026 · View profile
Academic milestones
Recognition, training, and international experience that shape the research.
2024
PhD in Engineering
Cum Laude · International Mention.
2022
International research stay
National Technical University of Athens.
Applied developments
Selected technical developments.
Examples of optimization methods implemented as software, simulation workflows, and planning tools.
Roadworks Planning Optimization
Problem. A roadworks schedule can look feasible on paper and still create serious disruption across the network.
Approach. Bayesian optimization linked to Aimsun simulations and scenario-based impact assessment.
My role. I developed the workflow used to compare candidate schedules through transport simulation.
Dynamic Fleet Routing & Rerouting
Problem. Passenger and parcel fleets need to react when requests and operating conditions change during service.
Approach. Dynamic routing, fleet coordination, and real-time replanning for demand-responsive operations.
My role. I am developing the optimization methods for the project’s Munich passenger-and-parcel use case.
Evolutionary & Metaheuristic Optimization
Problem. Difficult search spaces demand a careful balance between exploring new regions and refining good solutions.
Approach. Evolutionary algorithms, hyper-heuristics, adaptive simulated annealing, reinforcement learning, and diversity control.
My role. I design, implement, and benchmark these methods across routing, engineering, and image-analysis problems.
Collaboration
Interested in collaborating?
I welcome collaborations in artificial intelligence, computational intelligence, optimization, and applied R&D, including scientific research, European projects, and technology development.
Get in touch