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 as Assistant Professor, where he currently teaches master's courses on Electronic Systems, Industrial Automation, Autonomous Mobile Robots, and Deep Learning. Since June 2019 he is also the Coordinator of the M.Eng. in Smart Industry.
He combines his teaching duties with research projects at the Institute for Research in Technology (IIT), mainly on topics related to energy efficiency and artificial intelligence applications. As a result of his work on energy efficiency at the IIT, on May 2018 he co-founded a spin-off called Stemy Energy, where he currently serves as CTO.
Areas of interest:
Energy efficiency and flexibility · Internet of Things (IoT) · Deep learning · Computer vision · Autonomous mobile robots · Topological modeling of the environment · Industry 4.0
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: C, C++, Python, SQL, MATLAB, LaTeX
- Operating systems: Windows, macOS, Linux, FreeRTOS
- Cloud computing: Google Cloud
- Frameworks and libraries: TensorFlow, OpenCV, ROS, Django, ORY
- DevOps tools: Git, Docker, VirtualBox, Conan
- Software: CoppeliaSim, TIA Portal
- Hardware: Raspberry Pi, Google Coral, dsPIC, Arduino, Particle, ESP32, Intel RealSense cameras
Current research interests:
IoT energy efficiency platforms, Explainable artificial intelligence (XAI), Computer vision and deep learning applied to industrial manipulators and mobile robots, Tangible simulators for urban planning
Güitta-López, L., Boal, J., López López, A.J. 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] 
JCR: 5,300 Q2 (2022) - SJR: 1,145 Q2 (2022)
JCR: 4,474 Q1 (2020); 5,600 Q1 (2022) - SJR: 0,798 Q1 (2020); 1,625 Q1 (2022)
JCR: 2,219 Q2 (2019); 4,200 Q1 (2022) - SJR: 0,609 Q1 (2019); 0,968 Q1 (2022)
JCR: 1,554 Q4 (2016); 2,700 Q3 (2022) - SJR: Q2 (2016); 0,704 Q1 (2022)
JCR: 0,824 Q4 (2015); 2,700 Q3 (2022) - SJR: 0,704 Q1 (2022)
JCR: 0,688 Q4 (2014); 2,700 Q3 (2022) - SJR: Q2 (2014); 0,704 Q1 (2022)
JCR: 4,159 Q1 (2013); 9,000 Q1 (2022) - SJR: Q1 (2013); 1,989 Q1 (2022) 
