About Me

Robotics and Machine Learning Engineer

Experienced mechatronics engineer with demonstrated hands-on experience in industry and academia in machine learning and robotics. Recognised team player in startups and prominent open-source projects, excelling in dynamic, self-directed, and challenging environments.

My work is the intersection of three circles:
  • Robotics
  • Machine Learning (and MLOps)
  • Embedded Software Engineering.

  • My interests in robotics are, but not only, Visual and LiDAR-based odometry, SLAM, Model-based control algorithms, trajectory planning, and perception models optimisation (for resource-constrained devices and safety-critical tasks).

    My experience in machine learning spans across different fields. From recommendation systems, statistical modelling Bayesian inference, physical systems modelling and time series analysis, to state-of-the-art vision models (such as 2D/3D object detection, classification, and tracking, 2D instance segmentation, 3D point cloud instance segmentation).

    I have experience with production-ready software and safety-critical systems requiring real-time performance. I have experience maintaining software stacks deployed across fleets of active in-field robots. I have an "I can do it then do it" mindset from years of working in startup environment.

    I am an open-source advocate. This portfolio is to share my work with everyone. I love applying research in real life. You'll always find me learning about something new.

    I am always open to collaboration in research and open-source work. If you find one of my projects interesting or want me on your project, do not hesitate to contact me :)

    I am currently a robotics software engineer at Kingdom technologies Ltd, Glasgow, UK.

    Résumé & Experience


    You can find my career experience on my LinkedIn page and in my résuméavailable in this section.

    Publications & Projects

    Click on any project name or image for more details.

    Publications

    Bayesian Knowledge Tracing For Assessment Results Analysis

    This was a collaboration research project between Discovery Education and Uniparticle.
    Bayesian Knowledge Tracing, a Hidden Markov model, is utilised to analyse the results of assessments. The technique was tested and validated with a real-life dataset from a coding competition.

    Projects

    VSLAM Playground

    A full Visual SLAM pipeline using state-of-the-art deep learning-based feature descriptors and matchers (SuperPoint, DISK, SuperGlue, LightGlue) and deep stereo depth estimation.

    Depth Yolact ROS

    A ROS wrapper for yolact instance segmentation with depth image extension for 3D bounding boxes and pointcloud segmentation. The package has different pointcloud filtering techniques (K-means & Gaussian filtering).

    IDeepify

    A robust deep learning-based Know Your Custmer (KYC) for Egyptian national IDs. (face detection, face matching, Arabic OCR, ID validation)

    Deep Computer-Aided Sperm Analysis (CASA)

    Faster R-CNN object detection and a modified DeepSORT were used to detect and track human spermatozoa in phase-contrast, dark-field, and bright-field microscopy imaging. The recorded tracks are then used to calculate and report various parameters.

    I can only share the given details about this project.

    ROS Package: move_base_sequence

    A ROS Action server to handle communication with move base action server to navigate through a list of waypoints. The package is available on ROS (Kinetic/Melodic/Noetic) for installation.

    D435i VIO

    D435i camera is solely used to obtain reliable visual-inertial odometry and SLAM.


    Undergraduate Projects

    RHex platform

    A project made at the robotics lab of University of Central Lanchasire (UCLan). Funded by a grant from Erasmums+.

    RowBoat

    The project targeted plastic waste in rivers using an autonomous boat for picking the plastic waste from the water.

    Automated production line

    Undergradaute course project. A fully automated assembly/disassembly low-cost production line. Won the 1st place.

    Contact Me

    If you have any question, project to work on, or anything to discuss, do not hesitate to contact me on my email or LinkedIn. Click on the email to get the email address copied to your clipboard (click on the envelope in the side panel to get redirected to send an email) or on LinkedIn to get redirected to my LinkedIn account. Thank you in advance.