Talks and presentations

Talks

Invited talks, seminars, and public presentations from the team.

Diversity in the Human Brain

2026-06-15OHBM satellite event, Brain Mappers of Tomorrow

A public presentation exploring diversity, language, and the human brain.

Laterality in Language: diversity in the human brain

We all look different on the outside—but what does diversity look like on the inside?


As part of the international 'Brain Mappers of Tomorrow' initiative, Mehdi Ketata and Dr. Sandrine Muller conducted a presentation for students at Paulette Collavet elementary school in French to promote diversity and inclusion in the context of neuroimaging.

The goal of this intervention was to raise awareness among children regarding the diversity of human brains and how each individual processes information differently. Through a presentation tailored to their age group (between 8 and 11 years old), students from two classes discovered some fundamental principles of brain function, the fMRI method used in neuroscience to study the human brain, as well as the importance of cognitive and linguistic diversity. The primary educational objectives were to provide a simple introduction to a research method used in cognitive neuroscience—functional MRI—to sensitize students to the fact that human brains share both similarities and differences, and to promote the value of individual diversity while fostering a positive view of the differences between people.

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Health Data Integration and Exploitation in imaging

2026-03-30Connecting Expertise Webinar, Swansea University, UK

A webinar on multiscale AI for imaging and population data science.

Empowering Clinical Decision-Making through Multiscale AI

How can we move from seeing a lesion on a scan to truly understanding its molecular driver?


The research of Dr. Sandrine Muller sits at the intersection of AI and medicine, focusing on the integration of complex, multi-scale biological data.

By developing tools that link genomic-level data to macroscopic imaging, Dr. Miller’s work aims to transform medical imaging from a descriptive tool into a predictive one. Her research emphasizes the importance of "explainable AI"—ensuring that the next generation of diagnostic tools are not only highly accurate but also transparent, reliable, and seamlessly integrated into the clinical workflow.