Reda Oulbacha
Verified Expert in Engineering
Artificial Intelligence Developer
Reda earned his M.Sc doing Computer Vision Machine Learning research at the University of Montréal, École Polytechnique. He published at IEEE and Wiley, and published a PCT patent application. He then built real-world Computer Vision AI systems in the logistics and transportation safety industries. His previous projects include a PyTorch framework for medical images, a CV method for 3D CT to 2D X-ray image fusion, and a kernel pruning method for YOLOv5. Reda loves working on innovative products.
Portfolio
Experience
Availability
Preferred Environment
C++, PyTorch, TensorFlow, OpenCV, NVIDIA CUDA, Scikit-learn, Scikit-image, ARKit, Python, Data Science
The most amazing...
...thing I've worked on is a method for MRI-guided spine surgery using CycleGAN with very limited data, which we published in a peer-reviewed scientific journal.
Work Experience
Machine Learning Developer
Artera
- Developed a Kubernetes Native AI batch inference job monitoring solution using Grafana and Prometheus, allowing the company to have observability on its production workloads.
- Built a distributed AI batch inference engine using Kubernetes Native workflow orchestration, modernizing the company's inference infrastructure.
- Introduced a unified AI Model registry, allowing the cross-functional teams to centralize AI models and facilitate cross-functional collaboration.
Machine Learning Developer
BusPatrol
- Contributed to several computer vision projects using state-of-the-art CNN and vision transformers to solve a boundary detection problem with over 98% accuracy and optimized the neural networks to double the speed gain on CPU inference.
- Led the deployment of a machine learning (ML) lifecycle management infrastructure on AWS, increasing efficiency and reproducibility of ML workflows internally and easing collaboration with the DevOps team.
- Addressed a business-critical problem, which was first thought to need AI, through simple data aggregation and analysis. The surfaced insights brought a simple way to solve the issue, saving the company significant time and cost.
- Deployed production-grade ML Inference Infrastructure to AWS as microservices using AWS CDK and AWS SageMaker Endpoints.
Machine Learning Developer
Faimdata
- Developed the company's first optical character recognition algorithms and pipeline in Python, C/C++, PyTorch, TensorRT, and DeepStream, succeeding with 95% accuracy in the first pilot project, leading to the company acquiring the client's business.
- Applied transfer learning from the first deployments to a new use-case, reproducing similar performance levels, succeeding in a second pilot project that led to the acquisition of a second client's business.
- Reduced iteration time by 25% after establishing workflow best practices in ML deployment iterations using Docker, Google Cloud Platform, Git, Data Version Control, and model testing.
- Pruned deep neural networks (DNNs) to 60% faster runtime, allowing for the use of more affordable hardware and reducing hardware expenses by 15%.
- Hired and trained a new team member and transferred knowledge to help grow and scale the company's computer vision team.
Experience
Development of a PyTorch Framework for Medical Images
https://github.com/Roulbac/GanSegA Computer Vision Tool for 3D CT to 2D X-ray Image Fusion
https://github.com/Roulbac/2D3DAutoRegIntegration of a Structured Pruning Method for YOLOv5
https://github.com/Roulbac/yolov5/tree/feature/torch_pruning_integrationARKit iOS Application to Denoise the Camera Pose Using CoreML on a DJI Gimbal
Skills
Languages
Python, C++, Swift
Libraries/APIs
PyTorch, TensorFlow, OpenCV, Scikit-learn, SciPy, NumPy, Node.js, Pandas
Tools
Scikit-image, NVIDIA Jetson, You Only Look Once (YOLO), CMake, Amazon SageMaker, ITK, Xcode, DJI SDK, Amazon EKS
Paradigms
Data Science
Other
NVIDIA TensorRT, DeepStream SDK, Machine Learning, Image Processing, Computer Vision, Deep Learning, Numba, Artificial Intelligence (AI), Convolutional Neural Networks (CNN), Object Detection, Generative Adversarial Networks (GANs), Medical Imaging, Machine Learning Operations (MLOps), Sequence Models, Infrastructure as Code (IaC), 3D Image Processing, Augmented Reality (AR), Time Series, Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT)
Frameworks
Core ML, ARKit
Platforms
Google Cloud Platform (GCP), Docker, Amazon Web Services (AWS), NVIDIA CUDA, iOS, Kubernetes
Education
Master's Degree in Biomedical Engineering
École Polytechnique (Affiliated with University of Montréal) - Montréal, Québec, Canada
Master's Degree in Electrical Engineering
INSA Lyon - Lyon, France
Certifications
DeepLearning.AI TensorFlow Developer Professional Certificate
Coursera
Deep Learning Specialization
Coursera
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