Walid M.

OrthoCabinet is an IT solution developed with Django (backend) and React (frontend), designed for the management of speech therapy practices.

I designed and implemented the backend architecture in Python/Django, including data modeling, patient appointment management, access security, and the setup of REST APIs for communication with the frontend.

Particular work was done on performance optimization, especially ORM relationship management, SQL query optimization, pagination, and filter structuring (dates, practitioners, patients).

The project integrates a PostgreSQL database, with a focus on maintainability, scalability, and understanding the business needs of healthcare professionals.

The project "Claim Severity Prediction" aims to estimate, using artificial intelligence models, the potential financial cost of a claim for each insured person based on their characteristics and history. This approach allows insurance companies to anticipate costs, adjust their premiums, and optimize risk management.

We developed a claim severity prediction model that analyzes insured profiles and their history to estimate the potential cost of a claim. This project includes data collection and structuring, selection of relevant variables, choice and implementation of prediction algorithms, and evaluation of results to optimize risk management.

During my 6-month internship at CNRS (UMR LMR 9008, Reims), I worked on a research project aimed at better understanding the impact of stent placement on the cerebral venous network.

My main tasks were:

Segmentation of MRIs and extraction of complex venous structures.

Preprocessing of medical data in Python to produce usable geometric models.

Generation of meshes adapted to the complexity of veins with MMG.

Numerical simulations (FreeFem++) to analyze blood flow and evaluate the effect of the stent.

This project allowed me to develop solid expertise in complex data processing, Python automation, and scientific modeling.