In research, our engineering team plays an important role: we support our researchers’ projects and accelerate their dissemination.
We develop reliable and replicable open-source code for rapid publication with the aim of making their discoveries known to the public, increasing the visibility of the research outcomes, and bridging the gap between research and industrial applications.
We assist researchers for a 3-month period throughout the entire life cycle of their Data Science project, starting from the initial idea to the training of the model and its deployment, including essential tasks like data collection, feature engineering, model training, and deployment.
our past projects
POT
The POT repository on GitHub is a Python Optimal Transport library that provides several solvers for optimization problems related to optimal transport in areas such as signal processing, image processing, and machine learning. It aims to offer efficient solutions for transport-related challenges across various applications in AI and data science.
This project enhances the Pyronear forest fire detection system by incorporating advanced image processing techniques such as data augmentation, super-resolution, and hyperparameter tuning, all aimed at improving machine learning model robustness for faster and more reliable smoke detection.
The scikit-network repository on GitHub is a Python library for the analysis of large-scale graphs and networks. It provides fast and efficient algorithms for tasks such as clustering, ranking, classification, and visualization, making it a valuable tool for working with complex network data in machine learning and data science applications.
XPER (eXplainable PERformance) is a methodology that uses Shapley values to measure the contribution of input features to a model’s predictive performance (e.g., AUC, R²), focusing on performance rather than predictions, and includes SHAP as a special case.
At Hi! PARIS, we help you manage your projects from start to finish, from creation to exploitation of the idea. The team is made up of machine learning engineers.
We support you on your machine learning project by working in close collaboration with you.
The Hi! PARIS Engineering Team is made up of engineers specializing in Data and artificial intelligence. This initiative is intended to bring added value to researchers in the development of their project.
Support the development of research projects.
Promotion of reproducible research.
Bring ad hoc expertise to research teams.
Maintain and deploy a data factory (a development platform).
Provide the necessary technical resources.
Promote the work of researchers.
Capitalize on research projects.
Encourage collaborations between schools in order to deepen research by mutualizing resources.