Biography:
Jaime Boal obtained the degrees of Industrial Engineer (major in Electronics), M.Sc. in Research in Engineering Systems Modeling, and PhD from Comillas Pontifical University in 2010, 2012, and 2014, respectively. After finishing his PhD, he joined the Electronics, Control and Communications Department, where he currently teaches courses on Electronic Systems and Autonomous Mobile Robots. Since June 2019 he is also the Coordinator of the M.Eng. in Intelligent Industry.
He combines his teaching duties with research projects at the Institute for Research in Technology (IIT), mainly on topics related to robotics, artificial intelligence applications, and energy efficiency. As a result of his work on energy efficiency at the IIT, in May 2018 he co-founded a spin-off called Stemy Energy, in which he served as CTO until June 2024.
Areas of interest:
Autonomous mobile robots · Computer vision · Reinforcement learning · Internet of Things (IoT) · Industry 4.0 · Energy efficiency and flexibility
Experience:
Throughout his career he has focused on the tight integration of hardware and software. He has designed and developed electronic control boards and real-time software for embedded systems, connected these devices to cloud servers using IoT protocols with end-to-end encryption and mutual authentication, and implemented applications capable of managing multiple concurrent connections with low latency. He has also worked on the design of platforms based on microservices interconnected through REST APIs.
Skills:
- Languages: Spanish (native), English (C2), French (B1)
- Programming languages: Python, C, C++, SQL, MATLAB, LaTeX
- Operating systems: Windows, macOS, Linux, FreeRTOS
- Cloud computing: Google Cloud
- Frameworks and libraries: TensorFlow, OpenCV, ROS, ORY
- DevOps tools: Git, Docker
- Software: CoppeliaSim, TIA Portal
- Hardware: Raspberry Pi, Google Coral, dsPIC, Arduino, Particle, ESP32, Intel RealSense cameras
Current research interests:
Computer vision and reinforcement learning applied to industrial manipulators and mobile robots, Graph neural networks (GNN) generation, Explainable artificial intelligence (XAI), IoT energy efficiency platforms
L. Güitta-López, V. Suriani, J. Boal, A.J. López López, D. Nardi, "Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators", Robotics, Vol. 14, nº. 7, pp. 86, July 2025. [Online: June 2025] doi: 10.3390/robotics14070086
I. de Rodrigo, A.J. López López, J. Boal, "VERSE: Visual Embedding Reduction and Space Exploration - Latent-space clustering for improving document understanding", Pattern Recognition, Vol. 180, nº. Part D, pp. 114448, December 2026. [Online: July 2026] doi: 10.1016/j.patcog.2026.114448
L. Güitta-López, J. Boal, A.J. López López, "Evaluating the Perception, Understanding, and Forgetting of Progressive Neural Networks: A Quantitative and Qualitative Analysis", AI, Vol. 7, nº. 4, pp. 120, April 2026. [Online: March 2026] doi: 10.3390/ai7040120
I. de Rodrigo, A. Sánchez-Cuadrado, J. Boal, A.J. López López, "The MERIT Dataset: Modelling and efficiently rendering interpretable transcripts", Pattern Recognition, Vol. 172, nº. Part B, pp. 112502, April 2026. [Online: September 2025] doi: 10.1016/j.patcog.2025.112502
L. Güitta-López, V. Suriani, J. Boal, A.J. López López, D. Nardi, "Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators", Robotics, Vol. 14, nº. 7, pp. 86, July 2025. [Online: June 2025] doi: 10.3390/robotics14070086
L. Güitta-López, J. Boal, A.J. López López, "Learning more with the same effort: how randomization improves the robustness of a robotic deep reinforcement learning agent", Applied Intelligence, Vol. 53, nº. 12, pp. 14903 - 14917, June 2023. [Online: November 2022] doi: 10.1007/s10489-022-04227-3