CV
Download my CV (PDF) using the button above. Also available from my [GitHub profile](https://github.com/AlejandroDopico2) and [dopico.ai](https://alejandrodopico2.github.io/dopico.ai/).
Basics
| Name | Alejandro Dopico-Castro |
| Label | PhD Researcher in Frugal AI | Continuous & Federated Learning |
| alexdopico2001@gmail.com | |
| Url | https://alejandrodopico2.github.io/ |
| Summary | PhD researcher at the University of A Coruña (LIDIA Group) focusing on frugal AI, continual learning, and federated learning for resource-efficient systems. |
Work
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2026.03 - Present Porto, Portugal
Research Stay (Visiting Researcher)
INESC TEC, University of Porto
Research stay with João Gama (University of Porto) and Pedro Henriques Abreu (University of Coimbra).
- Collaboration on deep learning and continual learning for resource-efficient settings.
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2025.03 - Present A Coruña, Spain
PhD Researcher
LIDIA Group, University of A Coruña
- Research on frugal AI methods (continual, federated, few-shot learning) for resource-constrained systems.
- Development of adaptive learning modules within the EU PILLAR-Robots project.
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2024.01 - 2025.02 A Coruña, Spain
Research Support Technician (MSc Internship)
LIDIA Group, University of A Coruña
- Developed class-incremental learning models for dynamic environments within the PILLAR-Robots project.
- Work evolved into current PhD research on Frugal AI.
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2022.10 - 2023.06 A Coruña, Spain
AI Software Developer
Cinfo Company
- Designed and optimized deep learning models (YOLO, SSD) for automated sports production.
- Integrated C++ real-time inference with Python-based training pipelines.
Education
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2025.03 - Present A Coruña, Spain
PhD
University of A Coruña (UDC)
Computer Science
- Funded PhD on Frugal and Sustainable AI (continual, federated, few-shot learning).
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2023.09 - 2025.02 A Coruña, Spain
MSc
University of A Coruña (MIA-UDC)
Artificial Intelligence
- Thesis: Efficient Single-Step Framework for Incremental Class Learning
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2019.09 - 2023.06 A Coruña, Spain
Publications
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2026.01.01 FedHENet: A Frugal Federated Learning Framework for Heterogeneous Environments
ESANN 2026
Single-round, hyperparameter-free federated image classification with homomorphic encryption. Authors: Alejandro Dopico-Castro, Óscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Amparo Alonso-Betanzos, Ivan Perez Digón.
Skills
| Programming | |||||
| Python | |||||
| SQL | |||||
| C++ | |||||
| Shell | |||||
| ML Frameworks | ||||||
| PyTorch | ||||||
| TensorFlow | ||||||
| Scikit-Learn | ||||||
| OpenCV | ||||||
| HuggingFace | ||||||
| Tools | ||||||
| Docker | ||||||
| Git | ||||||
| MLFlow | ||||||
| DVC | ||||||
| CI/CD | ||||||
| Cloud | ||
| AWS (EC2, S3) | ||
| Research Areas | ||||||
| Continual Learning | ||||||
| Federated Learning | ||||||
| Frugal AI | ||||||
| Edge AI | ||||||
| Computer Vision | ||||||