His research interests focus on explainable machine learning, computational scientific discovery, and semantic technologies, all in the context of artificial intelligence for science. His group has developed machine learning methods that learn explainable models from complex data in the presence of domain knowledge: these include methods for multi-target prediction, semi-supervised and relational learning, and learning from data streams, as well as automated modelling of dynamical systems.

Professor Džeroski has lead (as coordinator) many national and international (EU-funded ) projects and has participated in many more. He is also the technical coordinator of the Slovenian Artificial Intelligence Factory. The work of Professor Džeroski has been extensively published and is highly cited: with more than 27000 citations and an h-index of 75 (in the GoogleScholar database), Professor Džeroski is the most frequently-cited computer scientist in Slovenia (according to the 2025 ranking by Research.com). 

In knowledge-driven modelling, an expert derives a model based on their knowledge of the domain studied: Both the structure and the parameters of the model are derived by the expert from knowledge about the entities and processes in the modelled system. In data-driven modelling, many model structures are considered in a trial-and-error fashion, their parameters are fit to data, and a complete model is returned: This is typically a black-box process that does not take into account domain knowledge. Explainable scientific models need to be expressed in formalisms accessible to humans and learned through approaches that integrate data-driven and knowledge-driven modeling and use both data and domain knowledge.

The talk will discuss approaches to integrating data-driven and knowledge-driven construction of scientific models. Different formalisms for representing models and domain knowledge will be discussed, including process-based models and context-free grammars. We will conclude with a discussion of recent approaches that rely on the use of probabilistic context-free grammars and other generative models for equation discovery and place our work in the broader context of Artificial Intelligence for Science. 

In this talk, Manish Gupta will present two multimodal retrieval systems that address challenging computer vision problems: image search and video moment localization. He will introduce novel frameworks that leverage diverse input modalities (including text, sketches, and video) to interpret complex user intent and context. He will begin with Composite Sketch + Text Based Image Retrieval, a new paradigm for image search that uses hand-drawn sketches to capture hard-to-name objects and text to describe attributes or interactions that are difficult to sketch. He will then move to the temporal domain with Video-to-Video Moment Retrieval, where a query video is used to precisely localize a semantically corresponding event within a longer target video. Together, these works demonstrate a unified vision: advanced multimodal alignment models are essential for enabling robust, fine-grained retrieval across images and videos, especially when user intent is nuanced, composite, or hard to express through any single modality. 

He has led AI and machine learning projects at large corporations, universities, and startups. He shaped national and regional AI policy as Secretary of Innovation, Science, and Technology for the State of Rio Grande do Sul, Brazil, and held senior academic executive roles at the MIT Sloan’s Legatum Center for Development and Entrepreneurship and the Federal University of Rio Grande do Sul. At Boeing, he directed global AI and ML teams and co-authored the company’s first formal AI Design Practice. As Secretary, he organized the department from scratch, built eight regional innovation ecosystems, and led the evidence-based COVID-19 scientific and data response for 11 million residents, earning a #1 innovation ranking in Brazil (Center for Public Leadership, 2021–2022).

As a startup advisor and mentor, he has guided science- and technology-based ventures at the Creative Destruction Lab (CDL-Seattle, University of Washington). He organized and taught Impact Ventures: Building Innovation-driven Startups in Global Growth Markets at MIT Sloan’s Legatum Center for Development and Entrepreneurship, helping founders and students navigate AI strategy, product development, and growth in competitive global markets.

A pioneer in Neurosymbolic AI and trustworthy AI systems, Lamb co-authored Neural-Symbolic Cognitive Reasoning (Springer, 2009) and has published over 100 peer-reviewed papers at premier venues including IJCAI, AAAI, and NeurIPS. He holds a Ph.D. in Computer Science from Imperial College London and an MBA from the MIT Sloan Fellows Program. Drawing on decades of experience at the intersection of AI research, corporate deployment, public policy, and venture building, Lamb advises organizations on AI strategy, governance, responsible innovation, and the transition from research to real-world impact. 

At the ELLIT Focus Period, Giorgos Papanastasiou will explore what happens when causal reasoning meets generative AI, and why their union may be essential for trustworthy machine intelligence in healthcare and science. Today’s most powerful AI systems learn from correlations rather than causes, leaving them brittle under distribution shifts, hard to interpret, and prone to confounder-induced spurious associations. Drawing on Pearl’s causal hierarchy, from association to intervention to counterfactuals, this talk shows how causal AI contributes structure through causal graphs, the do-operator, and individual-level counterfactual reasoning, while generative AI contributes the capacity to synthesize, imagine, and create at scale. Together they address each other’s limitations: causality grounds generative models in robustness and trustworthiness, while generative modeling lets causal systems produce rich counterfactual outputs such as images, molecules, and clinical reports. Dr Papanastasiou will illustrate this synergy through his own recent work, including large-scale causal modeling in healthcare, confounder-aware foundation models in drug discovery, benchmarks for counterfactual image generation, methods to identify confounding effects in time series and images, and LLM-driven causal discovery that can revolutionize scientific discovery at scale. The result is a compelling vision of causal generative AI spanning diagnosis, treatment planning, and drug discovery in medicine; and hypothesis generation, experimental design, and discovery from observational data in science, machines that move beyond asking “what” to genuinely reasoning about “why.”

Throughout his career, Edson has worked on several initiatives related to Standardization, Robotics, Artificial Intelligence and Ethics of Artificial Intelligence in Academia, Industry, and International and Multilateral Organizations. For instance, Edson is a Member of the Global Commission on Responsible Artificial Intelligence in the Military Domain; South America Ambassador at IEEE TechEthics; Chair of the IEEE RAS/SA 7007—Ontologies for Ethically Driven Robotics and Automation Systems Standardization Working Group; Vice-Chair of the IEEE RAS/SA Ontologies for Robotics and Automation Standardization Working Group; Member of the ACM Global Technology Policy Council; Former Member of the United Nations Secretary-General’s High-level Panel on Digital Cooperation; Former Member of the UNESCO Ad Hoc Expert Group (AHEG) for the Recommendation on the Ethics of Artificial Intelligence and Former Member of the Global Future Council on the Future of Artificial Intelligence and of the G20 Digital Agenda Working Group at World Economic Forum.

This talk examines the role of missing data in shaping AI systems and their societal impact. Rather than focusing only on biased data, it highlights how what is excluded, ignored, or never collected plays a crucial role in how these systems are built and how they function. The talk situates these omissions within broader questions of power, showing how they can silence certain voices, overlook lived experiences, and reinforce existing inequalities. It also reflects on how AI systems are embedded in wider structures that shapes what becomes visible and what remains invisible. Finally, it discusses the need to move beyond narrow technical and ethical framing, and instead considers more justice-oriented and care-centered approaches to designing and developing AI systems.

He is internationally recognized as one of the top 2% most influential scientists (Stanford ranking) and a laureate of the prestigious Fulbright IMPACT Award. He received the “Frontiers in AI” distinction from Adam Mickiewicz University for his contributions to transparent and socially responsible AI. His work has appeared in leading venues such as Nature Machine Intelligence, NeurIPS, ICML, ECCV, CVPR, and AAAI. He has delivered invited talks at major conferences including ECML and ECAI and has co-organized workshops on explainable and trustworthy AI at NeurIPS, AAAI, ECAI, and ECML-PKDD.

Professor Biecek is the creator and maintainer of widely used open-source packages for model interpretability (e.g. DALEX, auditor) and an active contributor to standardization efforts in credible AI. He has served on program committees of top-tier conferences, advised European institutions on AI safety and ethics, and collaborated with industry partners on deploying interpretable models in high-stakes domains.

Beyond research, he is strongly engaged in education and outreach. He founded the Smarter Poland Foundation and promotes AI literacy through comic books, courses, and science communication. His interdisciplinary work at the interface of statistics and computer science has established him as a leading voice in the global debate on reliable and responsible AI.

Jim Tørresen’s research interests include artificial intelligence, ethical aspects of AI and robotics, machine learning, robotics, and applying this to complex real-world applications. Several novel methods have been proposed. He has published more than 300 peer-reviewed papers in international journals and conferences. He has given more than 50 invited talks/keynotes at international conferences and institutions and 21 tutorials at international conferences during the last 10 years. He is in the program committee of more than ten different international conferences, associate editor of three international scientific journals as well as a regular reviewer of a number of other international journals. He has also acted as an evaluator for proposals in EU FP7 and Horizon2020 and is currently project manager/principal investigator in three externally funded research projects/centers. He is a member of the Norwegian Academy of Technological Sciences (NTVA) and the National Committee for Research Ethics in Science and Technology (NENT), where he is a member of a working group on research ethics for AI. 

Prof. Luís A. de Lima Silva holds B.Sc. and M.Sc. degrees in Computer Science from the Federal University of Rio Grande do Sul (UFRGS) and a Ph.D. in Computer Science from University College London (UCL, UK). He is affiliated with the Department of Applied Computing at the Federal University of Santa Maria (UFSM), where he teaches in the Computer Science, Information Systems, and Computer Engineering programs, and is a permanent member of the Graduate Program in Computer Science (PPGCC/UFSM). His research focuses on the development and application of Artificial Intelligence (AI) methods, including Deep Learning, Generative Adversarial Networks (GANs), Transfer Learning, Explainable AI (XAI), Clustering, Reinforcement Learning, and Case-Based Reasoning. His current research spans Cybersecurity, Defense, Health, Environment, and Computer Games. In Defense, he has participated in the SIS-ASTROS projects since 2004, a partnership between UFSM and the Brazilian Army focused on simulation systems for the tactical training of ASTROS artillery batteries. His research includes agent-based simulation, path planning in large-scale virtual environments, and AI-based decision support. He currently coordinates a FAPERGS-funded project on Transfer Learning, Transformers, and GAN architectures for synthetic data generation in Digital Health and Cybersecurity. He is also a member of the CIARS network project on AI for healthcare and a tutor in the PET Saúde Digital UFSM program, an initiative of the Brazilian Ministry of Health. His research also addresses Deep Learning models for climate resilience of river basins and road ecology, as well as algorithms for competitive and collaborative multi-agent games involving imperfect information and bluffing, particularly the card game Truco. Previously, he participated in the Petrographer Project on Knowledge Engineering for petroleum reservoir assessment and conducted research on AI for the authentication and dating of historical paintings.

This talk will briefly reflect on more than a decade of research and development in simulation-based training, carried out through a long-term collaboration among researchers from different areas of Computer Science (CS) at the Federal University of Santa Maria (UFSM), a researcher from the Federal University of Rio Grande do Sul (UFRGS)—both universities located in southern Brazil—and the Brazilian Army. I will first present how the Integrated Simulation System ASTROS (SIS-ASTROS) project has evolved through three major R&D initiatives, from the development of an integrated virtual environment for tactical simulation to current efforts aimed at providing broader and more reusable agent-based simulation capabilities. I will then discuss how practical simulation requirements have progressively given rise to research problems in Artificial Intelligence (AI) and other areas of Computer Science. Among the various military needs addressed throughout the project, I will highlight my research group’s work on scalable pathfinding over large, realistic terrains; hierarchical and topography-aware path planning; learning-based heuristics; and interactive autonomous agents designed to support training at multiple echelon levels within the same simulation environment. From this perspective, the talk will focus on the research and engineering challenges that emerge from real-world simulation-based training involving ASTROS artillery batteries and groups. Finally, I will discuss lessons learned from this long-term experience, including challenges in applied AI, human–autonomy interaction during simulation sessions, iterative co-development with military experts, and the relationship between sustained applied research and the development of operational simulation systems.