Speakers

Title: Affective Intelligence in the Wild: Real-Time Engagement, Emotion, and Satisfaction Analytics for Human-Centred Events
Abstract: Affective computing is moving beyond controlled laboratory settings toward real-world, dynamic, and socially complex environments where emotions, attention, engagement, and satisfaction emerge from subtle multimodal cues. This talk presents a research trajectory on privacy-preserving affective analytics for live events, crowded environments, exhibition stands, service queues, retail spaces, and audience-centred experiences. Building on recent work on microscopic engagement estimation using gaze and posture, holistic architectures for computer-vision-based audience analysis, crowd counting, emotional body gesture recognition, multimodal sentiment classification, and longitudinal queue analysis, it discusses how soft computing methods can transform noisy, partial, and uncertain human behavioural signals into actionable collective intelligence. The central theme is the transition from individual cues to group-level and crowd-level affective understanding. The proposed perspective integrates gaze, posture, body expression, spatial dynamics, crowd density, queue behaviour, sentiment indicators, and multi-sensor fusion into scalable models capable of operating in real time and, when appropriate, at the edge. Rather than focusing on identity recognition, the approach emphasises anonymous and privacy-aware behavioural interpretation, enabling organisers, service providers, and decision-makers to understand how people attend, move, interact, wait, engage, and respond to physical experiences. The talk concludes by outlining open challenges for affective computing in the wild: dataset scarcity, ecological validity, multimodal uncertainty, explainability, GDPR-aware sensing, real-time deployment, edge processing, and the ethical transformation of affective computing into trustworthy decision-support systems for human-centred environments.

Title: AI for Science and Engineering: Leveraging GNNs, LLMs, XAI, and Nanocombinatorics
Abstract: The increasing availability of data from the first three paradigms of science (experiments, theory, and simulations), along with advances in artificial intelligence and machine learning (AI/ML) techniques has offered unprecedented opportunities for data-driven science and discovery, which is the fourth paradigm of science. Within the arena of AI/ML, deep learning (DL) has emerged as a game-changing technique over the last decade with its ability to effectively work on raw big data, bypassing the (otherwise crucial) manual feature engineering step traditionally required for building accurate ML models, thus enabling numerous real-world applications. In this talk, I will present some of the ongoing AI/ML/DL research in our group with illustrative real-world applications in materials science and engineering, by leveraging graph neural networks (GNNs), large language models (LLMs), explainable AI (XAI), and generative AI (GenAI). We will also see how AI can be used to accelerate nanocombinatorics workflows to facilitate rapid structure characterization of megalibraries with millions of nanoparticles on a chip.

Edgar Weippl

University of Vienna, Vienna, Austria

Marco Dorigo

Université Libre de Bruxelles, Brussels, Belgiuma

Ahmad Bazzi

New York University Abu Dhabi, UAE

Tatiana Kalganova

Brunel University of London, London, UK

Bharat Bhargava

Purdue University, Indiana, USA

Sardar Islam

Victoria University, Melbourne, Australia

Witold Pedrycz

University of Alberta, Alberta, Canada

Dimitrios A. Karras

National and Kapodistrian University of Athens, Greece

Massimiliano Cannata

SUPSI, Canobbio, Switzerland

Unnati Shah

Utica University, Utica, NY, USA

Donatella Firmani

Sapienza University of Rome, Rome, Italy

Hong Nhung Nguyen

Gachon University, Seoul, South Korea

Theofanis P. Raptis

Institute of Informatics and Telematics, National Research Council (CNR), Pisa, Italy

Flora Ferreira

University of Minho, Portugal

Xun Shao

Toyohashi University of Technology, Aich, Japan

Ashis Jalote Parmar

Norwegian University of Science and Technology, Torgarden, Norwaye