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I'm Mohamed Aziz OMRI, an AI Engineer based in Bordeaux, France. I design and implement machine learning and medical imaging pipelines to support DBS planning and neuromodulation therapies, focusing on image registration, electrode segmentation, and predictive modeling to personalize treatment. Over the past years I have built end-to-end workflows in Python (TensorFlow, PyTorch, scikit-learn) and visualization tools to help clinicians interpret post-operative imaging and validate preprocessing pipelines. I enjoy collaborating with researchers and clinicians to translate complex data into actionable insights.…I'm Mohamed Aziz OMRI, an AI Engineer based in Bordeaux, France. I design and implement machine learning and medical imaging pipelines to support DBS planning and neuromodulation therapies, focusing on image registration, electrode segmentation, and predictive modeling to personalize treatment. Over the past years I have built end-to-end workflows in Python (TensorFlow, PyTorch, scikit-learn) and visualization tools to help clinicians interpret post-operative imaging and validate preprocessing pipelines. I enjoy collaborating with researchers and clinicians to translate complex data into actionable insights.

Mohamed Aziz OMRI

AI Engineer, Data Scientist, Programmer, +1

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I'm Mohamed Aziz OMRI, an AI Engineer based in Bordeaux, France. I design and implement machine learning and medical imaging pipelines to support DBS planning and neuromodulation therapies, focusing on image registration, electrode segmentation, and predictive modeling to personalize treatment. Over the past years I have built end-to-end workflows in Python (TensorFlow, PyTorch, scikit-learn) and visualization tools to help clinicians interpret post-operative imaging and validate preprocessing pipelines. I enjoy collaborating with researchers and clinicians to translate complex data into actionable insights.…I'm Mohamed Aziz OMRI, an AI Engineer based in Bordeaux, France. I design and implement machine learning and medical imaging pipelines to support DBS planning and neuromodulation therapies, focusing on image registration, electrode segmentation, and predictive modeling to personalize treatment. Over the past years I have built end-to-end workflows in Python (TensorFlow, PyTorch, scikit-learn) and visualization tools to help clinicians interpret post-operative imaging and validate preprocessing pipelines. I enjoy collaborating with researchers and clinicians to translate complex data into actionable insights.

Available to hire

I’m Mohamed Aziz OMRI, an AI Engineer based in Bordeaux, France. I design and implement machine learning and medical imaging pipelines to support DBS planning and neuromodulation therapies, focusing on image registration, electrode segmentation, and predictive modeling to personalize treatment.

Over the past years I have built end-to-end workflows in Python (TensorFlow, PyTorch, scikit-learn) and visualization tools to help clinicians interpret post-operative imaging and validate preprocessing pipelines. I enjoy collaborating with researchers and clinicians to translate complex data into actionable insights.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Work Experience

AI Engineer at RebrAIn

March 1, 2024 - Present

Designed and implemented end-to-end image registration pipelines to align post-operative CT scans with pre-operative MRI data; developed semi-automated segmentation workflows to extract DBS electrode trajectories and contact positions from CT scans; built and trained ML/DL models to predict stimulation parameters based on imaging features and anatomical proximity to brain targets.

AI Research Intern at RebrAIn

March 1, 2023 - August 1, 2023

Developed and optimized a medical image registration pipeline to align post-operative CT scans with pre-operative MRI volumes; implemented semi-automatic segmentation to localize DBS electrodes; designed and built a 3D validation interface for multimodal alignments and segmented electrodes.

Deep Learning Intern at dB.Sense

July 1, 2022 - September 1, 2022

Implemented a speaker diarization system based on LSTM embeddings with unsupervised clustering for multi-speaker segmentation; preprocessed audio data, trained LSTM models, and evaluated with DER and confusion error; optimized robustness for noisy/overlapping speech.