Synthetic face datasets are increasingly used to overcome the limitations of real-world biometric data, including privacy concerns, demographic imbalance, and high collection costs. However, many existing methods lack fine-grained control over identity attributes and fail to produce paired, identity-consistent images under structured capture conditions. In this talk, I will present FLUXSynID, a framework for generating high-resolution synthetic face datasets with user-defined identity attribute distributions and paired document-style and trusted live capture images. The dataset generated using FLUXSynID shows improved alignment with real-world identity distributions and greater diversity compared to prior work. I will also discuss how FLUXSynID’s dataset and generation tools can support research in face recognition and morphing attack detection (MAD), enhancing model robustness in both academic and practical applications.

Digital platforms generate unprecedented traces of human behaviour, offering new methodological approaches to understanding collective action, polarisation, and social dynamics. Through analysis of millions of digital traces across multiple studies, we demonstrate how online behaviours predict offline action: Brexit-related tribal discourse responds to real-world events, machine learning models achieve 80% accuracy in predicting real-world protest attendance from digital signals, and social validation through "likes" emerges as a key driver of mobilization. Extending this approach to conspiracy narratives reveals how digital traces illuminate psychological mechanisms of belief and community formation. Longitudinal analysis of YouTube conspiracy content demonstrates how narratives systematically address existential, epistemic, and social needs, while examination of alt-tech platforms shows how emotions of anger, contempt, and disgust correlate with violence-legitimating discourse, with significant differences between narratives associated with offline violence versus peaceful communities. This work establishes digital traces as both methodological innovation and theoretical lens, demonstrating that computational social science can illuminate fundamental questions about polarisation, mobilisation, and collective behaviour across contexts from electoral politics to conspiracy communities.

The focus of this talk is not about my research in AI or Robotics but my own journey on trying to do research and understand intelligence in a rapidly evolving research landscape. I will trace my path from conducting early-stage research during graduate school, to working on practical solutions within a startup environment, and finally to my current role where I participate in more structured research at a major tech company. Through these varied experiences, I will provide different perspectives on research and talk about how my core beliefs on intelligence have changed and sometimes even been compromised. There are no lessons to be learned from my stories, but hopefully they will be entertaining.

In this 10-minute talk, followed by a scientific discussion, I will present my SNSF Starting Grant project, PREDICT, which investigates the neural and behavioural mechanisms underlying cognitive development from infancy through toddlerhood, childhood, and early adolescence. By bridging developmental neuroscience with advanced data analytics, PREDICT challenges traditional, often isolated methodologies and sets unique landmarks for understanding the interplay between early sleep markers, functional connectivity, and multisensory integration. Recognizing that understanding developmental trajectories requires multimodal approaches, PREDICT integrates cutting-edge methodologies - including 128-channel electroencephalography, actigraphy, eye-tracking, vestibular stimulation, and immersive VR - combined with cognitive testing, machine learning, and computational methods to: • Build predictive models and identify neural and behavioural markers of developmental trajectories. • Develop open-science protocols and pipelines for multimodal developmental data integration. Fostering an interdisciplinary, collaborative, and multimodal framework, PREDICT establishes benchmarks for the field, driving scientific, societal, and policy impacts by informing education, enhancing child health, and guiding early interventions, and delivering lasting benefits to researchers, practitioners, and policymakers.

The history of dress and fashion constitutes an essential yet frequently underappreciated dimension of art theory. While garments in visual art have long functioned as carriers of symbolism, identity, and social codes, dress analysis remains a marginal subject within mainstream art historical discourse. Despite the disciplinary challenges posed by its non-institutionalized status, scholars such as Rosita Levi Pisetzky—and many others—have produced foundational research that continues to shape our understanding of visual culture and material identity. This presentation draws on a multidisciplinary theoretical framework encompassing dress history, art history, social history, and semantics. It investigates the methodological potential of iconographic dress analysis in Early Modern artworks, with a particular focus on how clothing functions not merely as decoration but as a semiotic system embedded in the image. The aim is to explore how dress contributes to the construction of meaning within the pictorial space—revealing not only what is seen, but how and why it is seen. Moreover, what possible bridges can be built at the intersection of dress iconography and Artificial Intelligence in the study of Early Modern Art History?