Hadi Koubeissy
Researcher Phd Candidate
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolHadi Koubeissy is a PhD candidate and researcher at BMW TechOffice in Munich with six years of experience building AI, machine learning, and computer vision solutions for industrial applications. He blends hands-on MLOps and backend development with DevOps and team leadership, having led training and deployment optimizations for YOLO and TensorFlow projects used within BMW Innovation Lab. Previously a tech lead at inmind.ai, he has practical expertise in accelerating large-scale training pipelines, multi-core and GPU optimizations, and production-ready Docker configurations. His work shows attention to reliable observability and tooling—improving TensorBoard integration, custom APIs, and GUI-driven no-code training experiences. Fluent in moving research into production, he pairs academic rigor from his PhD studies with a pragmatic focus on digital transformation in manufacturing. An often-overlooked strength is his track record of improving developer ergonomics in ML workflows, making complex training setups accessible to non-experts.
6 years of coding experience
1 year of employment as a software developer
Bachelor of Engineering - BE, Computer and Communication Engineering (Software Engineering), Bachelor of Engineering - BE, Computer and Communication Engineering (Software Engineering) at Université Antonine - UACollège Notre Dame des Soeurs Antonines - NabatiehDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at UBFC - Université Bourgogne-Franche-Comté
Github Skills (23)
docker10
python10
pandas10
machine-learning10
dockers10
data-preprocessing10
tensorboard10
darknet10
deep-learning10
dataprep10
trainings10
tensorflow10
object-detection10
modeling10
data-engineering9
Programming languages (6)
Github contributions (5)
BMW-InnovationLab/BMW-TensorFlow-Training-GUI
Jun 2020 - Jan 2023
This repository allows you to get started with a gui based training a State-of-the-art Deep Learning model with little to no configuration needed! NoCode training with TensorFlow has never been so easy.
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 12 PRs, 18 pushes in 2 years 7 months
Contributions summary:Hadi contributed to the training and data preparation pipelines for deep learning models within the repository. Their work included enhancing training time and data preparation for large datasets, adjusting data shuffling, and fixing import issues in the training modules. The user also modified the model evaluation service to improve the training process. Furthermore, they adjusted the checkpoint writing interval based on training steps.
pythongui-basedno-codestate-of-the-arttraining
BMW-InnovationLab/BMW-YOLOv4-Training-Automation
Jul 2020 - May 2022
This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our BMW-LabelTool-Lite and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. NoCode training with YOLOv4 and YOLOV3 has never been so easy.
Role in this project:MLOps Engineer
MLOps EngineerContributions:1 release, 27 commits, 2 PRs in 1 year 10 months
Contributions summary:Hadi focused on improving the training and deployment process for YOLO models. They implemented support for YOLOv4 and optimized performance by enabling multi-core CPU and CuDNN-Half for GPUs. The commits show the user addressed issues with TensorBoard integration, enabling custom port usage and correcting paths for data visualization. They also adjusted Docker configurations for both CPU and GPU environments to ensure proper TensorBoard port forwarding.
monitorno-codestate-of-the-arttrainingyolo-tensorboard
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