Céline Robardet

Céline Robardet

Dr Céline Robardet, Professor at INSA Lyon, LIRIS UMR 5205

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Pages

Posts

Blog Post number 4

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Blog Post number 3

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Blog Post number 2

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Blog Post number 1

less than 1 minute read

Published:

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collaborations

portfolio

projects

GRAISearch (2014-2018)

Published:

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.

RESALI (2015-2019)

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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.

Academics (2018-2021)

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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.

Portrait (2022-2026)

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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.

Wait4 (2022- 2027)

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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.

publications

Pattern management challenges

Published in Workshop on Pattern Representation and Management, 2004

Recommended citation: Vassiliadis, Panos et al.(2004). "Pattern management challenges". Workshop on Pattern Representation and Management.
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Mining Bi-sets in Numerical Data

Published in Knowledge Discovery in Inductive Databases, Workshop, 2006

Recommended citation: Besson, Jérémy et al.(2006). "Mining Bi-sets in Numerical Data". Knowledge Discovery in Inductive Databases, Workshop.
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Evolving networks

Published in Mining Massive Data Sets for Security, NATO, 2007

Recommended citation: Borgnat, Pierre et al.(2007). "Evolving networks". Mining Massive Data Sets for Security, NATO.
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Constraint-Based Subspace Clustering

Published in SIAM International Conference on Data Mining, SDM, 2009

Recommended citation: élisa Fromont et al.(2009). "Constraint-Based Subspace Clustering". SIAM International Conference on Data Mining, SDM.
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Triggering patterns of topology changes in dynamic graphs

Published in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM, 2014

Recommended citation: Kaytoue, Mehdi et al.(2014). "Triggering patterns of topology changes in dynamic graphs". IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM.
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Local Pattern Detection in Attributed Graphs

Published in Solving Large Scale Learning Tasks. Challenges and Algorithms - Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday, 2016

Recommended citation: Boulicaut, Jean-François et al.(2016). "Local Pattern Detection in Attributed Graphs". Solving Large Scale Learning Tasks. Challenges and Algorithms - Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday.
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Contrastive Antichains in Hierarchies

Published in SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, 2019

Recommended citation: Bendimerad, Anes et al.(2019). "Contrastive Antichains in Hierarchies". SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD.
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Sampling Rank Correlated Subgroups

Published in Distributed Computing and Artificial Intelligence, DCAI, 2019

Recommended citation: Hammal, Mohamed-Ali et al.(2019). "Sampling Rank Correlated Subgroups". Distributed Computing and Artificial Intelligence, DCAI.
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Improving the non-urgent sanitary transportation

Published in International Conference on Control, Automation and Diagnosis, ICCAD, 2023

Recommended citation: Chane-Haï, Timothée et al.(2023). "Improving the non-urgent sanitary transportation". International Conference on Control, Automation and Diagnosis, ICCAD.
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Transparent Explainable Logic Layers

Published in European Conference on Artificial Intelligence, ECAI, 2024

Recommended citation: Ragno, Alessio et al.(2024). "Transparent Explainable Logic Layers". European Conference on Artificial Intelligence, ECAI.
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Trend Mining in Dynamic Attributed Graphs

Published in Machine Learning and Knowledge Discovery in Databases, ECML PKDD, 2013

Recommended citation: Desmier, Elise et al.(2013). "Trend Mining in Dynamic Attributed Graphs". Machine Learning and Knowledge Discovery in Databases, ECML PKDD.
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Improving the Quality of Rule-Based GNN Explanations

Published in Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD, 2022

Recommended citation: Kamal, Ataollah et al.(2022). "Improving the Quality of Rule-Based GNN Explanations". Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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