Collaborated on the development of an extension using 3DSlicer application to enhance segmentation of MRI/CT images efficiency.
Contributed to the GitHub repository for the SlicerCart extension.
Engaged with faculty members at the University of Montreal to enhance knowledge and skills.

What it does
An interactice map to assign a health index to Quebec territory based off of terrain data ranging from soil composition to proximity to mines.

How we built it
We built Arborescent using a full-stack approach:

Model: We trained a Random Forest Regression model to identify the health index of every single pixel of Quebec's map to then classify its risk category. This is done by extracting 7 time-series Google Earth Engine Satellite Imaging layers and risk data through various maps of Quebec, such as protection regions and regions with mining activity.

Frontend: We utilized React and Next.js to create a dynamic and interactive user interface. This allowed us to efficiently manage client-side rendering and provide a seamless user experience.

Backend: For the server-side logic, we employed Python. This choice enabled us to leverage Python's extensive libraries for data processing and analysis. We used Flask to handle API requests and manage data interactions.

Tracks tracks workout reps in real-time by using OpenCV and machine learning to monitor shoulder and wrist movements. It then adjusts the BPM of your music to match your workout intensity, seamlessly integrating with Spotify. The website automatically adjusts the music's tempo based on how fast or intense the user’s workout is, providing a more personalized and energetic experience. It’s free to use, and users can choose their music genre, while the app handles intensity adjustments.

Request for grants from Collège Jean-de-Brébeuf for the purchase of several
servers from the Montreal Jewish hospital
Installation of Linux and Folding@Home software to help scientists around the
world simulate protein synthesis to find cures for cancer and Alzheimer's
Head of the communications team and management of all relations with the
grants committee and sponsors

Utilized linear regression models to classify SARS-CoV-2 variants based on
specific genome diversification
Led project under a mentor and presented project to co-InspiritAI Scholars'
program participants

Built responsive app using Flask, Python and SQL that allowed users to learn a
language through repetitive pronunciation practicing and listening
Built features using Deepgram-API, OpenAI-API to provide a responsive
language learning experience
Released it for free for Collège Jean-de-Brébeuf students
Submitted as my personal project and credited