Abdelrahman Abubakr
AI Team Lead at avatarin
Tokyo, Japan
Summary
👤
Senior🎓
Top SchoolAbdelrahman Abubakr is an AI Team Lead and machine learning engineer with nine years of experience specializing in deep learning, computer vision and robotics, currently based in Tokyo. He leads the Computer Vision sub-team at avatarin, building on-device inference pipelines and practical products like virtual avatars for human–robot interaction and luggage size estimation for airports. His background spans research and industry—from INRIA publications on real-time activity recognition and domain-invariant features to deploying ultra-low-latency models on neuromorphic chips and NVIDIA Jetson edge devices. Abdelrahman combines academic rigor (MSc Grenoble) with hands-on systems work in C++ and embedded inference, and a knack for translating synthetic-to-real domain adaptation research into robust industrial solutions. An interesting thread through his career is turning research ideas (domain randomization, augmented autoencoders) into production features that preserve privacy by processing on-device.
9 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Electrical, Electronics and Communications Engineering, Bachelor's degree, Electrical, Electronics and Communications Engineering at Alexandria UniversityM2 Master of Science in Informatics at Grenoble (MoSIG), GVR, Computer Science, M2 Master of Science in Informatics at Grenoble (MoSIG), GVR, Computer Science at National School of Computer Science and Applied Mathematics of GrenobleM1 Master of Science, Computer Vision and Robotics, M1 Master of Science, Computer Vision and Robotics at Université de Bourgogne
Arabic, English
Github Skills (28)
keras-tensorflow10
object-detection-api10
detection-model10
face-detector10
object-detection9
efficientnet8
color-detection8
semantic-segmentation8
classifier8
algorithms7
caffe7
tensorflow7
deep-learning7
ncnn6
android6
Programming languages (4)
Github contributions (5)
a64bit/Pedestrian-Detection
Jul 2017 - Oct 2017
Evaluation of state-of-the-art object detection algorithms for the task of pedestrian detection. The algorithms include; Faster-RCNN, RPN+BF, YOLO, and SSD.
Contributions:35 commits, 34 pushes, 1 branch in 3 months
faster-rcnnobject-detection
a64bit/tf2-object-detection-api-tutorial
Jul 2020 - Jul 2020
Tensorflow 2 Object Detection API Tutorial. This tutorial will take you from installation, to running pre-trained detection model, and training your model with a custom dataset, then exporting it for inference.
Contributions:7 commits, 6 pushes, 1 branch in 8 days
detection-modelinferenceobject-detection-apitensorflowtf2
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