Data Scientist and Computer Vision Engineer with experience in AI, deep learning, and computer vision across startup and research environments. Worked on hand gesture recognition, image classification, object detection, …
Data Scientist and Computer Vision Engineer with experience in AI, deep learning, and computer vision across startup and research environments. Worked on hand gesture recognition, image classification, object detection, pose estimation, and medical image processing using Python, TensorFlow, Keras, OpenCV, Julia, C++, and Qt.
Project: Development of Hand gesture recognition system with the aim of detecting these gestures in the interior vehicle and performing corresponding actions
upon detection (Ex: Open the radio). To achieve this:
•I have created a hand object detection model from simple image which helps us in
our principal system. I have started this task from data collection, to final model
testing.
•I have created the hand gesture recognition model and test it on public dataset
•I have worked on Image pre-processing and Augmentation to make the model
generalize with different environments and different corner cases (ex: luminosity,
rotation, scale)
•I have create a post-processing algorithm which can detect gestures form a realtime continuous video using our created model. ,
Keywords: Python, Pytorch, Object Detection, Pose estimation.
Created a hand object detection model from simple images, from data collection to final model testing.
Created a hand gesture recognition model and tested it on a public dataset.
Worked on image pre-processing and augmentation to improve generalization across different environments and corner cases such as luminosity, rotation, and scale.
Created a post-processing algorithm to detect gestures from real-time continuous video using the created model.
Participated in the improvement of AI processes from data labeling to model prediction, including image classification with different CNNs.
Created and maintained the quality of the company’s databases.
Managed the data labeling team.
Implemented an object detection model that detects tennis players and rackets from an image.
Developed and tested neural networks to predict the 2D pose of a tennis player from a photo using Kincube’s own database.
Developed and tested neural networks to predict the 3D pose of a tennis player using weakly supervised learning methods with a public database.
Implemented an autoencoder from scratch without using deep learning libraries with Julia.
Trained the autoencoder on molecular data.
Implemented a medical image processing application using C++ and Qt.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
