Fernando Jurado-Lasso
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F. Fernando Jurado-Lasso
Engineer specializing in IoT, Networked Embedded Systems, and Machine Learning for communications and networking
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About Me

I am an experienced Engineer specializing in IoT, Networked Embedded Systems, and Machine Learning for communications and networking. With a background as a postdoctoral researcher at DTU Compute, Technical University of Denmark, I have acquired a Ph.D. in Engineering from The University of Melbourne, focusing on Software-Defined Wireless Sensor Networks (SDWSNs). Additionally, I hold an M.Eng. in Telecommunications Engineering from The University of Melbourne and a B.Eng. in Electronics Engineering from Universidad del Valle, Colombia.

I am passionate about addressing challenges in IoT ecosystems, particularly in resource management, network optimization, and intelligent runtime adaptation. My research interests revolve around the intersection of IoT, embedded systems, and ML, where I strive to develop innovative solutions to real-world problems.

In addition to my research contributions, I have been actively involved in teaching and mentoring activities. Serving as a Head Tutor and Guest Lecturer for various courses, I have provided comprehensive guidance and fostered a collaborative learning environment for students. I have also supervised several undergraduate and postgraduate students in their research projects, guiding them through the research process and helping them develop their technical skills.

Recent News

  • Mar 18, 2025: Paper Accepted in IEEE Internet of Things Journal!
  • May 31, 2024: HRL-TSCH Paper Accepted in IEEE Transactions on Cognitive Communications and Networking Journal!
  • Mar 18, 2024: ELISE Paper Accepted for Publication!
  • Jan 28, 2024: New preprint on Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement Learning
  • Jan 18, 2024: Introducing HRL-TSCH: Revolutionizing IIoT Scheduling with Hierarchical Reinforcement Learning for TSCH Networks
  • May 31, 2023: Introducing ELISE: A Reinforcement Learning Framework for TSCH Protocol Optimization in IoT Networks
  • Feb 1, 2022: Paper Accepted in IEEE Access!
  • Oct 8, 2021: Latest Preprint in Advancements in ML-SDWSNS Research!
  • Oct 1, 2021: New Academic Position!
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Research Interests

  • Internet of Things (IoT)
  • Networked Embedded Systems
  • Energy-Efficient Networking
  • Reinforcement Learning (RL)
  • Deep Q-Network (DQN)
  • Multi-Agent Systems

2018-2025 F. Fernando Jurado-Lasso. Except where otherwise noted, all text and images licensed under Creative Commons CC BY 4.0

ORCID 0000-0002-5005-781X PGP public key   Fingerprint:
FC00 72B7 B1ED B725 95A5  35E2 C7FF 3CFD 3347 1693

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