The topic of this work is about suicide detection. He is particularly focused in making machine learning and deep learning especially explainable for human understanding.
Interpretability of distribution models of plant species communities learned through deep learning - application to crop weeds in the context of agro-ecology.
This 3 month internship consisted in the study of NoSQL databases and more precisely of the MongoDB document oriented database and the Neo4J graph database.
This internship led to the realization of a tutorial as well as a set of exercises to facilitate the use of these databases.
Rider Carrion Cleger
This six-month internship led to the creation of a fully generic multi-site document-sharing platform.
Each site allows the creation and identification of users. A multi-site search engine lets you retrieve relevant documents regardless of where they are stored.
Yonatan Carranza Alarcon
This 6 month internship consisted in creating a mathematical model to randomly generate crowdsourcing data based on predefined user profiles.
The realization of this model is based on the idea of distributions of a priori probability and random draws.
Finally, the responses of each user are themselves drawn randomly according to the profile of each user.
Ghofrane Ben Hamed
This 6 month internship consisted in developing an Android version of The Plant Game, thus leading to more attendance. This mobile app relies on web services to access The Plant Game Services and
publish results obtained locally.
Alban Lorillard & Raphael Barraud
This 3 month internship at BT, Adastral Park, Ipswich, consisted in developping a dynamic workflow management system in order to help building machine learning pipelines for anomaly detection.
This 6 month internship at LIRMM consists in proposing various machine learning models in order to predict species spatial distribution.
A 6 month internship to analyse the ability of deep species distribution models to predict in novel geographical area.
Lucas Bernigaud Samatan
A 3 month internship to study the variation in species predictions near and within agricultural plots in order to better understand the interactions between farming practice and biodiversity.