Mohamed Nagy
Graduate Research Associate
Abu Dhabi Emirate, United Arab Emirates
Summary
👤
Senior🎓
Top SchoolMohamed Nagy is a perception and multi-object tracking researcher and engineer with nine years of experience focused on autonomous vehicles and sensor fusion. Currently a visiting researcher at TUM and a PhD candidate at Khalifa University, he develops and deploys tracking algorithms on real autonomous platforms and contributes to perception engines in academic labs. His background blends hands-on robotics software (ROS, state estimation, SLAM) with teaching and industry-facing roles, including contributions to Udacity programs and summer schools at ETH Zürich. Mohamed has presented research at NSF/IAIFI workshops and applied his skills to space-related radiation tracking and production-grade backend development, showing a rare mix of academic rigor and practical deployment experience. Based in Germany, he brings a global footprint across research, education, and product evaluation in self-driving systems.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Electrical and Computer Engineering, Doctor of Philosophy - PhD, Electrical and Computer Engineering at Khalifa UniversitySoftware Engineering in Robotics, Robotics Technology/Technician, Software Engineering in Robotics, Robotics Technology/Technician at UdacityPreliminary MA, Computer Science, Preliminary MA, Computer Science at Helwan University Cairo
German, English, French
Stackoverflow
343reputation
25kreached
22answers
7questions
Github Skills (50)
kitti-dataset10
kitti10
object-tracking9
multi-object-tracking9
perception9
self-driving-car9
autonomous-driving9
transfer-learning8
robot7
computer-vision6
xml6
occlusion6
android6
kotlin6
semantic-segmentation6
Programming languages (5)
Github contributions (5)
MohamedNagyMostafa/Computer_Vission-SimpleCutting
May 2019 - May 2019
Contributions:14 commits, 12 pushes, 1 branch in 2 days
AV-Lab/RoadSceneUnderstanding-ModifiedUNet
Jun 2021 - Oct 2022
This project contains two experiments for semantic segmentation using a Modified Version of the U-Net model. The first experiment is semantic segmentation for one class, like cars, and the second experiment considers multiple classes.
Contributions:50 commits, 4 PRs, 2 pushes in 1 year 4 months
semantic-segmentationcomputer-visiondeep-leaningperceptiontransfer-learning
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