À propos de Muhammad
I am an AI and Machine Learning consultant specialising in medical imaging, biomedical signal processing, and deep learning. Based in Paris, I help healthcare organisations, MedTech startups, and research institutions turn complex medical data into reliable AI-powered solutions.
My expertise covers 3D convolutional neural networks for MRI analysis, EEG and ECG signal classification, object detection, and end-to-end ML pipeline development. During my research internship at CRMBM Aix-Marseille University, I built deep learning models for Multiple Sclerosis severity prediction from brain MRI, achieving significant improvement in classification accuracy using PyTorch and MONAI on HPC infrastructure.
I deliver reproducible, production-ready code with full documentation. Typical projects include feasibility studies, custom model development, explainability reports (Grad-CAM), and MLOps setup. I work remotely across France and Europe.
Anglais
Bilingue ou natif
Ourdou
Bilingue ou natif
Français
Notions
Accepte de travailler sur site
Paris (jusqu’à 50 km)
Expériences
- Aix–Marseille University, CRMBM–CEMEREM (La Timone)AI Research Engineer — Medical Imaging & Multiple SclerosisHIGH TECHfévrier 2025 - août 2025 (6 mois)FranceDeveloped a deep learning system to predict Multiple Sclerosis severity (EDSS score) from brain MRI scans, helping neurologists prioritise patient care. Built end-to-end 3D CNN pipelines in PyTorch/MONAI processing T1/T2 volumetric MRI data. Implemented custom loss functions to handle severe class imbalance, improving Macro-F1 classification accuracy significantly. Added Grad-CAM explainability maps so clinicians can visually verify model decisions. Full pipeline ran on HPC/SLURM with reproducible, audit-ready code.
Deliverables: Trained model, explainability reports, full documented codebase, reproducible training pipeline.
- UPEC — Université Paris-Est CréteilAI Engineer — Biomedical Signal & Image Processingoctobre 2023 - septembre 2025 (1 an et 11 mois)Developed multiple AI systems for biomedical and computer vision applications during M.Sc. in Biometrics & Intelligent Vision (final grade 15.985/20).Key projects:EEG/ECG anomaly detection — Built LSTM autoencoder for unsupervised classification of biomedical time-series signals. Designed preprocessing pipeline, trained on imbalanced datasets, evaluated with ROC/AUC metrics.Brain tumour segmentation — Implemented segmentation pipeline on MRI scans using classical image processing and deep learning approaches. Compared U-Net variants and evaluated Dice scores across tumour classes.Face biometrics desktop application — Built real-time identity verification system using facial recognition. Designed full pipeline from detection to feature extraction and matching.Drone detection — Trained Mask R-CNN for instance segmentation and detection of drones in aerial imagery. Handled small object detection challenges and custom dataset annotation.Eye-gaze tracking — Implemented behavioural analysis system using YOLOv5 for real-time gaze direction estimation.Deliverables across all projects: working models, evaluation reports, documented codebases in Python.
Tech: Python, PyTorch, TensorFlow, OpenCV, scikit-learn, YOLOv5, Mask R-CNN, LSTM, NumPy, Pandas, Git
- Gaichu (Kinettix), IslamabadTechnical Coordinatornovembre 2022 - août 2023 (9 mois)Islamabad, Islamabad Capital Territory, PakistanCoordinated multi-site field operations and engineer schedules across technical projects. Managed work orders, ticketing systems and project workflows. Supported on-site technicians and provided remote technical assistance. Ensured timely delivery across multiple concurrent projects.Tech: Project management, field operations, technical coordination
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Formations
- Master of Biometrics and Intelligent VisionUniversité Paris-Est Créteil (UPEC)2025
- B.Sc. TelecommunicationUniversity of Engineering and Technology (U.E.T.)2021