Feras Dayoub
Associate Professor (Machine Learning & Robotic Vision)
Adelaide, South Australia
Summary
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Senior🎓
Top SchoolFeras Dayoub is an associate professor and machine learning/robotic vision specialist with over a decade of experience translating computer vision research into real-world robotic systems. He has led and contributed to large collaborative efforts—most notably as Chief Investigator in Australian robotic vision centres—and co-leads visual learning research at QCR and IRL CNRS CROSSING. His work spans agricultural weed detection, AUVs protecting the Great Barrier Reef, and UAV-based infrastructure inspection, demonstrating a track record of deploying deep learning on mobile robots in challenging environments. He also teaches advanced robotics topics, emphasizing Bayesian approaches to localization, mapping and SLAM, and mentors the next generation of researchers. Less obvious is his consistent focus on robustness and deployability, bridging theoretical research and field-ready robotic vision solutions.
10 years of coding experience
Doctor of Philosophy (PhD) Computer vision for robotics, Doctor of Philosophy (PhD) Computer vision for robotics at University of Lincoln
English
Github Skills (3)
robot10
semantic9
robotics9
Programming languages (1)
Github contributions (5)
fdayoub/ros-semantic-mapper
Apr 2016 - Jul 2022
Place Categorization and Semantic Mapping on a Mobile Robot
Contributions:13 commits, 12 pushes, 1 branch in 6 years 4 months
placeroboticsrobotmappingsemantic-mapping
fdayoub/fdayoub.github.io
Dec 2019 - Jan 2025
Contributions:45 pushes, 1 branch in 5 years 2 months
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