Projects

Wait4 (2022- 2027)

Project type: France 2030 -- PEPR Agroécologie et numérique -- co-PI

The project WAIT4 (Welfare, Artificial Intelligence and new Technologies for Tracking key indicator Traits in animals facing challenges of the agroecological Transition) aims to enhance animal welfare (AW), a key component of sustainable livestock production systems. As environmental conditions become increasingly variable due to the impacts of global warming, the effects on animals raised in agroecological transition systems --characterized by less optimized and more variable outdoor conditions -- are expected to intensify. This underscores the growing need for innovative tools to assess AW and support decision-making, enabling the adoption of agroecological practices that promote animal well-being.

Portrait (2022-2026)

Project type: ANR PRCE -- PI

The project PORTRAIT (Improving psychiatric screening with artificial intelligence) is to develop an adaptive testing method that adjusts the questions of a test based on the subject’s previous responses. To achieve this, the project aims to extend recent advancements in recommendation systems and reinforcement learning methods to adapt tests dynamically. The project’s case study focuses on psychiatric tests, which represent a significant public health challenge. These tests currently incur high costs, but the approach developed within the project aims to reduce these costs while maintaining their reliability. A key challenge for adaptive testing is optimizing its administration to evaluate multiple psychiatric dimensions in a short period of time.

Academics (2018-2021)

Project type: Scientific breakthrough of IDEX Lyon -- Co-PI

The project ACADEMICS (mAChine LeArning & Data sciEnce for coMplex and dynamICal modelS) aimed to combine Machine Learning (ML) and Data Science (DS) to advance scientific research in two key directions:

• Computing and Information Processing: Developing new theoretical frameworks and learning algorithms tailored for complex scientific challenges involving heterogeneous, irregular, error-prone, dynamic, and intricate datasets, while incorporating relevant prior knowledge.
• Learning Complex and Dynamic Models: Harnessing the synergy between ML and DS to create data-driven models in two scientific domains: climate modeling and the quantitative understanding of social systems. These case studies addressed the critical challenge of learning sophisticated models from abundant, heterogeneous, and dynamic data.

RESALI (2015-2019)

Project type: Labex IMU (laboratory of excellence for the intelligence of urban worlds)

The primary scientific goal of the RESALI is to study the food networks and system. Feeding cities, particularly large urban agglomerations, both in quantity and quality, represents a significant challenge for the future of urban environments, especially in the context of sustainability and food justice. At the scale of urban food systems, there is a need for diagnostics to systematically understand the relationships between consumption hubs, food supply, and dietary behaviors. The RESALI project aims to test methods and tools for a detailed analysis of the organization of urban food systems, exploring the connections and disconnections between food supply and demand—essentially, between food resources and certain consumption basins, including the most marginalized and least informed populations.

GRAISearch (2014-2018)

Project type: European project -- call FP7-PEOPLE-2013-IAPP

The primary scientific goal of the GRAISearch project (Use of Graphics Rendering and Artificial Intelligence for Improved Mobile Search Capabilities) was to develop and integrate revolutionary graphics rendering and artificial intelligence (AI) methods into an existing social media search engine platform, creating groundbreaking mobile search capabilities with significant online commercial potential. The project leveraged pioneering technologies developed at two academic institutions -- INSA de Lyon (LIRIS UMR CNRS 5205) and Trinity College Dublin (TCD) -- in collaboration with the industry partner Tapastreet. The aim was to commercialize innovative functionalities that enhance social media search capabilities, positioning Tapastreet as a leader in this domain.