Francesco Fraternali
Senior Research Engineer at EpiSys Science, Inc.
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolFrancesco Fraternali is a Senior Research Engineer and PhD candidate who blends electrical engineering roots with nine years of hands-on research and development in AI-driven embedded and IoT systems. He designs and deploys deep reinforcement learning solutions that enable autonomous, maintenance-free sensor networks and has built a large-scale, batteryless energy-harvesting sensor deployment at UCSD to validate them in real smart-building contexts. At EpiSys Science he now applies multi-agent RL to coordinate swarms of unmanned drones for commercial and defense use, bridging academic rigor with production-oriented control systems. His work spans machine learning, embedded systems, and HCI, and includes practical advances in continual learning, transfer learning, and meta-RL to accelerate real-world adaptation. Notably, he has demonstrated that RL-based controllers can outperform state-of-the-art heuristics on constrained edge platforms, reflecting a rare combination of systems engineering and empirical validation. Based in San Diego, he brings cross-disciplinary fluency from supercomputing energy-efficiency research to cutting-edge autonomy deployments.
9 years of coding experience
8 years of employment as a software developer
University of California, San DiegoUniversity of BolognaMaster in Electrical Engineering, 4.0, Master in Electrical Engineering, 4.0 at University of Bologna, ITALYMaster Thesis (Exchange Student), Master Thesis (Exchange Student) at University of California, Los Angeles
Italian, English
Github Skills (20)
ros9
robotics-simulation8
gazebo-simulator8
ignition8
simulation8
gazebosim8
autopilot7
mavlink7
robotics7
drones7
raspberry-pi6
rover6
px46
simulator4
robot-simulator4
Programming languages (2)
Github contributions (5)
francescofraternali/Pible
Jun 2019 - Jun 2020
Contributions:16 commits, 24 pushes, 1 branch in 11 months
francescofraternali/Ray-RLlib-Pible
Jul 2019 - Nov 2019
Using Ray RLlib to test different Reinforcement Learning algorithms
Contributions:26 commits, 2 PRs, 37 pushes in 4 months
rayreinforcement-learning-algorithmsreinforcement-learningdeep-reinforcement-learninglearning-algorithms
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