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Mahdi Chamseddine received a Bachelor of Engineering in Electrical and Computer Engineering and a minor in Control and Robotics in 2014 from the American University of Beirut, Lebanon. He excelled academically, earning the Dean’s Award for Creative Achievement for his graduation project.
He went on to earn his Master’s degree in Electrical Engineering from the Technical University of Kaiserslautern, Germany in 2017. His area of focus was Automation and Control, and he wrote a thesis entitled “Detecting and Tracking Power Cables using Stereo Vision” as part of his research at the John Deere European Technology Innovation Center (ETIC) in Kaiserslautern. During his master’s studies, he held several research assistant positions at John Deere, DFKI, and the University of Kaiserslautern.
After his academic pursuits, Mahdi worked as a Product Engineer for two years at John Deere’s Advanced Engineering in Kaiserslautern. During this time, he honed his skills in radar and camera data processing and fusion.
In 2019, Mahdi joined the DFKI as a researcher and PhD candidate. He brings a wealth of expertise and a strong passion for his field, with research interests in the processing of radar, camera, and lidar data using machine learning techniques.
ONTOLOGY-BASED SEMANTIC LABELING FOR RGB-D AND POINT CLOUD DATASETS
Computing in Construction. European Conference on Computing in Construction (EC3-2023), July 10-12, Crete, Greece, EC3, 2023.
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Driving Activity Recognition Using UWB Radar and Deep Neural Networks
Sensors - Open Access Journal (Sensors) 23 2 Seiten 1-15 MDPI 1/2023 .
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Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network
International Conference on Pattern Recognition. International Conference on Pattern Recognition (ICPR-2020) January 12-15 Milano Italy IEEE 2021 .
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