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LIRIS Seminar - Prof. Mubarak SHAH: Fine-Grained Video Retrieval.
We are delighted to welcome Professor Mubarak SHAH to LIRIS on Friday, September 19, 2025. He will present his work on fine-grained video retrieval starting at 1:30 p.m. Meeting location: Gaston Berger Lecture Hall, LyonTech-La Doua Campus.
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The DM2L team publishes at NeurIPS 2025: “On Logic-based Self-Explainable Graph Neural Networks”
The DM2L team is pleased to announce that its paper “On Logic-based Self-Explainable Graph Neural Networks” has been accepted at NeurIPS 2025. In this work, Alessio Ragno, Marc Plantevit, and Céline Robardet introduce LogiX-GIN, a novel graph neural network architecture that is intrinsically explainable through logic rules.
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Yasmine Djebrouni – Honorable Mention for the GDR RSD 2025 Thesis Prize
The thesis is distinguished by an impressive experimental campaign and high-level publications. The jury praised the very solid methodology and the analytical rigor of the work on emerging topics at the frontier between systems research and machine learning.
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Bike-related theory and hands-on activities
A tutorial about how to ride a bike safely and a participatory mechanics workshop for the lab members took place on May 14 and 20.
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LIRIS Seminar - Prof. Ian DAVIDSON - Since AIs Will Rule Us, Let's Make Them Fair, Explainable, and Moral.
We are very pleased to announce Prof. Ian DAVIDSON's seminar at LIRIS, on Thursday, July 3, 2025, at 2 p.m., room FONTANNES (Charles Darwin Building D Ground floor). We look forward to seeing many of you!
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Hormur, a company leveraging LIRIS research and expertise in the media
In recent days, Hormur has been featured on France-Inter, Le Monde, TF1 and France Télévision. The platform enables artists and hosts to find each other and discuss to co-create artistic events in unusual locations. It also makes it extremely easy for the public to register to these events. Hormur benefited from a transfer of technology and the expertise from the SICAL team.
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Honoring Excellence: Wu Wenjun Award for Di Huang—Our Former PhD Student—and our Longtime Collaborators
We are pleased to share that Prof. Huang Di, along with Prof. Yunhong Wang and Ass.Prof. Hongyu Yang, has been awarded the First Prize in Natural Science at the 2024 Wu Wenjun Artificial Intelligence Science and Technology Awards—the highest national recognition in intelligent science and technology in China. Their research, "Efficient Representation Learning for Complex Visual Tasks", makes important theoretical and practical contributions by overcoming efficiency bottlenecks in visual representation learning across model design, data usage, and cross-domain transfer. The work has already seen impact across key sectors and has been praised for its simplicity, efficiency, and performance. We are especially proud of this achievement because Prof. Huang Di completed his PhD within the Liris lab at Ecole Centrale de Lyon, and it’s been a joy to see him grow into a leading researcher. We also value the long-standing scientific collaboration we’ve had with the group of Prof. Yunhong Wang and Prof. Di Huang—their continued excellence and dedication to advancing AI is so fruitful in our joint research topics. Congratulations to the entire team! This is a remarkable milestone and a strong step forward for the AI research community.
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Presentation of a new study on broken and aging smartphones to the French regulation authorities
Aurélien Tabard (SICAL team), presented to the Telecommunication regulation authority the results of the Broken Smartphones study. The study shows that over 40% of french people live with a dysfunctional smartphones, mostly due to software obsolescence.
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LIRIS Seminar - Emilie YU - Designing digital representations for digital art & computer-aided manufacturing.
We are pleased to announce Emilie YU's seminar at LIRIS, on Wednesday, June 25 at 2:00 p.m. (Nautibus Building, room C006, Campus LyonTech-la Doua). We look forward to seeing many of you!
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Exploring Bitcoin Transactions with ORBITAAL, the Largest Bitcoin Dataset Ever Built
As cryptocurrencies play an increasingly important role in both the economy and scientific research, access to reliable, comprehensive, and exploitable data has become essential. ORBITAAL ( cOmpRehensive BItcoin daTaset for temporAl grAph anaLysis) addresses this need by providing the largest dataset ever built from Bitcoin transactions, enabling in-depth analysis of network dynamics over nearly 13 years. How can such a dataset open new perspectives in network science, economics, or artificial intelligence?
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Laboratoire d'InfoRmatique en Image et Systèmes d'information
UMR 5205 CNRS / INSA Lyon / Université Claude Bernard Lyon 1 / Université Lumière Lyon 2 / École Centrale de Lyon