incoming PhD researcher · CMAP
Ayoub Oulad Ali
Applied Mathematics × Machine Learning.
I build mathematically grounded AI for scientific and medical imaging, with a focus on Generative AI and Physics-Informed Machine Learning.
BONUS The portfolio hides a playable retro handheld. Insert the coin.
LV. PHD
01 / ABOUT
Between mathematics,
physics and learning.
I am an Applied Mathematics graduate student at Sorbonne Université and an incoming PhD researcher at École Polytechnique (CMAP). My research blends Applied Mathematics with Scientific Machine Learning and Generative AI to advance Medical Imaging and computational imaging methods.
I focus on Physics-Informed Machine Learning and generative modeling that encode physical structure and uncertainty, enabling robust solutions in low-data clinical and scientific environments.
Read the full profile of Ayoub Oulad Ali ↗
Sorbonne Université
Research
Computational Imaging
02 / RESEARCH
Current research
2026École Polytechnique · CMAP
Physics-informed generative models for Mueller polarimetric imaging
Deep learning for biomedical polarimetric imaging, with a focus on robust and interpretable methods in low-data clinical settings.
- Deep generative architectures for Mueller matrix modelling
- Synthetic data generation for small clinical datasets
- Physical constraints: positivity and Mueller matrix realizability
- Uncertainty-aware learning and geometry-aware label smoothing
- Physically consistent representation learning
Research interests
VAEsDiffusion ModelsRepresentation Learning
Scientific MLBayesian Deep LearningComputer Vision
Uncertainty QuantificationExplainable AIProbabilistic Modeling
03 / TEACHING
Teaching
2026–27
course select ↗
Three teaching assignments across statistics, Monte Carlo methods and machine learning. Each course has a dedicated retro cartridge page for future TD, TP and correction resources.
APM_41033_EPAPM_51056_EPAPM_51434_EP
04 / PROJECTS
GitHub projects
all repositories ↗A curated view of public research code in generative modeling, machine learning, applied mathematics and scientific computing. Open the dedicated projects page ↗
05 / PATH
Academic path
PhD Research · CMAP
École Polytechnique
M2 · Applied Mathematics
Sorbonne Université
M1 · Applied Mathematics
Sorbonne Université
BSc · Mathematics & Computer Science
Université de Nîmes
06 / STACK
Tools & topics
$ cat stack.txt
ML PyTorch · Generative Models · JEPA · GNNs
Research Physics-Informed ML · Medical Imaging
Methods Bayesian DL · Uncertainty · Representation Learning
Focus Robustness · Interpretability · Data Efficiency
$ █07 / CONTACT
Let's build something
scientifically useful.
Open to research collaborations around generative AI, scientific machine learning and computational imaging.