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@MG2033
Flag of Egypt Shiekh Zayed City, Egypt
Member since August 14, 2018
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MG2033

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I'm a senior AI research engineer with 2.5+ years of experience in machine learning, deep learning, computer vision, and reinforcement learning. I've published many papers in reputable conferences: NIPS, CVPR, ICIP, IROS, ITSC. I'm the author of ShuffleSeg, a fast semantic segmentation network that was published in 2018. My AI skills include but not limited to: Python, C++, Tensorflow, PyTorch, OpenCV, Image Processing, Image Classification, Video Classification, Image/Video Segmentation, Image/Video Generation, GANs, Object Detection, Object Recognition, Numerical Computation, Git.
$30 USD/hr
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2.5
  • 100%Jobs Completed
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  • 88%On Time
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Portfolio

Recent Reviews

Experience

AI Research & Development Engineer

May 2019

Leading a state-of-the-art AI Research & Development team in Webville.

Co-founder of a Computer Vision Research Group

Jul 2017

• Implementation of state-of-the-art neural networks for computer vision tasks such as semantic segmentation, object detection, etc. • Publishing research ideas in reputed conferences.

Deep Learning Researcher

Apr 2017 - Aug 2018 (1 year)

• Conducted state-of-the-art research in deep learning application to autonomous driving. • Worked on combining computer vision area with deep learning techniques. • Implemented several research ideas on different platforms including real hardware and embedded systems such as: - Extracting information from LiDAR/Camera using semantic segmentation. - Building an end-to-end driving system using deep learning. - Using object detection as a part of a safety-critical driving system.

Co-founder of a Deep Learning Research Group

Mar 2017

Contributing to the open-source community by implementing, developing, and reproducing state-of-the-art algorithms.

Education

B.Sc., Computer Engineering

2014 - 2018 (4 years)

Qualifications

Top Student on Class (2018)

Faculty of Engineering, Cairo University

Publications

RTSeg: Real-time Semantic Segmentation Comparative Study

A Comparative Study of Real-time Semantic Segmentation for Autonomous Driving

ShuffleSeg: Real-time Semantic Segmentation Network

Realtime Semantic Segmentation Benchmarking Framework

Deep Convolution Long-Short Term Memory Network for LIDAR Semantic Segmentation

Real-Time Segmentation with Appearance, Motion and Geometry

Real-time Segmentation is of crucial importance to robotics related applications such as autonomous driving, driving assisted systems, and traffic monitoring from unmanned aerial vehicles imagery. We propose a novel two-stream convolutional network for motion segmentation, which exploits flow and geometric cues to balance the accuracy and computational efficiency trade-offs. The geometric cues take advantage of the domain knowledge of the application. In case of mostly planar scenes from high altitude unman

End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving

In this paper, we present a novel framework for urban automated driving based on multi-modal sensors; LiDAR and Camera. Environment perception through sensors fusion is key to a successful deployment of automated driving systems, especially in complex urban areas. Our hypothesis is that a well designed deep neural network is able to end-to-end learn a driving policy that fuses LiDAR and Camera sensory input, achieving the best out of both. In order to improve the generalization and robustness of the learned

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