LID projects
The team
Loïc Parrenin
Loic Parrenin is a student of Polytechnique Montreal at the LID (Data Intelligence Laboratory). He obtained his bachelor's and master's degrees in 2018 and 2019, respectively, in Industrial Engineering from Polytechnique Montreal, completing an integrated bachelor's-master's program (BMI). He is currently a Ph.D candidate in Industrial Engineering at Polytechnique Montreal.
His research interests are focused on data valorization in an industrial environment and particularly on the development of analytics tools to understand, improve and optimize manufacturing processes. His PhD project focuses on the production of organic flour. It consists of collecting and analyzing data present in the manufacturing process to develop and train machine learning models. These models allow a better understanding of the process by detecting the variables that influence it. Decision support tools and process optimization models can then be created.
At Polytechnique Montreal, he is involved in teaching by conducting practical work and projects to students around manufacturing information systems and connected objects. In LID, he is as well involved in different projects that focus on the relationship between video data processing and industrial robotics.
Ambre Dupuis
Ambre Dupuis is a student of Polytechnique Montreal at the LID (Data Intelligence Laboratory). She obtained a bachelor's degree in industrial engineering from Polytechnique Montreal in 2020 and a master’s degree in data valorization in 2021. She is currently a Ph.D. candidate in industrial engineering at Polytechnique Montreal.
Her research interests are focused on data valorization in an industrial context and particularly on the development of analytics tools for sustainable agriculture and lean manufacturing production. More precisely, her Ph.D. project consists of the development of sequential data analysis tools applied to different domains and concrete problems such as the forecasting of crop rotation or the prevision of plausible sequencing in production. Those previsions can then be used in decision support systems in order to evaluate a finite number of realistic scenarios.
At Polytechnique, she contributes to the support of students in difficulty by being a member of the Polytechnique Montreal Student Services (SEP) tutoring team and by leading multiple multimedia projects for the reception, orientation, and support of students, particularly during times of pandemic. She is also involved in numerous teaching projects in the Department of Mathematics and Industrial Engineering.
Other projects in which she is involved at the LID focus on the relationship between video data processing and industrial robotics.
Aimé Toumelin
Aimé Toumelin is a student of Polytechnique Montreal at the LID (Data Intelligence Laboratory). He obtained a bachelor's degree in Engineering Physics from Polytechnique Montreal in 2020 and is currently pursuing a master's degree in Industrial Engineering.
His research interests are focused on data valorization in an industrial context and particularly on the development of information systems applied to agriculture 4.0. More precisely, his master's project studies the development and implementation of collaborative decision support tools for decentralized SME networks. In Industry, he contributes to the development of fraud detection models for a Canadian e-commerce platform.
Other projects in which he is involved at the LID focus on the relationship between video data processing and industrial robotics.