Marcello Restelli is a Full Professor at the Department of Electronics, Information and Bioengineering at Politecnico di Milano, where he coordinates the Real-Life Reinforcement Learning Research Lab (RL3). He is the author of more than 200 international scientific publications, primarily focused on the study and development of new reinforcement learning techniques. His research results are applied to real-world problems through numerous industrial collaborations in diverse sectors, including finance, e-commerce, Industry 4.0, and automotive.

He is an ELLIS Fellow and serves as the research lead for the Artificial Intelligence Observatory of Politecnico di Milano. In 2020, he co-founded ML cube, a spin-off of Politecnico di Milano, where he is currently scientific advisor.

Olivier Sigaud is professor in Computer Science. He is member of the Machine Learning (MLIA) team in the Robotics Institute (ISIR) at Sorbonne University, Paris, France. His core expertise is in Reinforcement Learning, with 30 years of experience in the domain. More generally, he is interested in AI for robotics and developmental robotics. He also holds a PhD in philosophy since 2004 and has collaborated to Cognitive sciences and Computational Neurosciences projects. One of his recent focuses is in the use of foundational models for Robot learning. For more information, see https://www.isir.upmc.fr/personnel/sigaud/

Vignesh Ethiraj is the Co-Founder & CTO of NetoAI, a deep telecom technologist whose career spans hands-on network engineering, SDN innovation, and cutting-edge AI research. A core contributor to open-source SDN platforms including the ONOS controller — with contributions preserved in GitHub's Arctic Code Vault — Vignesh brings rare, ground-up expertise in both telecommunications infrastructure and AI. He created TSLAM, the world's first open-source telecom-specific large language model family, purpose-built to address the semantic and operational complexities of real-world telecom environments. He has published multiple research papers on domain-specific AI for telecommunications, covering areas such as telecom-specific embeddings, reasoning engines, and low-latency voice agents. Vignesh's work sits at the intersection of deep domain expertise and frontier AI research, with a mission to make telecom-native intelligence accessible to the global operator community.

Prof. Mykola Pechenizkiy is Chair of the Data Mining group at the Department of Mathematics and Computer Science, Eindhoven University of Technology. He is a founding Director of the Center for Safe AI. His research interests include several technical and socio-technical aspects of responsible AI, with a particular technical focus on evolving data and machine learning models. He has led and collaborated on several projects that have received international recognition, including the IDA 2023 Runner-up Frontier Prize, IEEE ICDE 2023 Best Demo Award, LoG 2022 Best Paper Award, ALA@AAMAS 2022 Best Paper Award, and the IEEE DSAA 2022 Best Paper Award. He serves in different roles on the committees of the leading AI conferences and journals. He will be a General Chair of ECMLPKDD 2027 to be hosted in Eindhoven.

Matthieu obtained his PhD from University Paul Verlaine of Metz, and his Habilitation from University of Lille, both in applied mathematics. He was an assistant professor at CentraleSupélec and a professor at University of Lorraine, from which he is currently on leave. Matthieu was one of the first members of Google Brain in France, and also spent some time at Cohere. He is now a principal scientist at Earth Species Project. His core research area is machine learning, especially everything revolving around sequential decision making, including in the context of LLMs, with applications to man-machine interactions. His broader research interests encompass signal processing, robotics and optimization. He is now focusing on foundation models for advancing the decoding of animal communication. 

Marco asked a swarm of AI agents about the next big thing. They negotiated, escalated, and concluded: “it depends on the infrastructure.” He took the hint. Marco’s research spans distributed systems, large-scale/cloud computing, and computer networking, with an emphasis on programmable networks. His current focus is building better systems support for AI/ML—practical implementations that deploy in the real world. He is a Professor of Computer Science at KAUST. Marco earned his Ph.D. in Computer Science and Engineering from the University of Genoa in 2009, after a visiting year at the University of Cambridge. He was a postdoctoral researcher at EPFL and a senior research scientist at Deutsche Telekom Innovation Labs & TU Berlin. Before joining KAUST, he was an assistant professor at UCLouvain. He has also held positions at Intel, Microsoft, and Google.

Nicola Cancedda is an independent AI researcher. Over 25 years, he held leadership positions at Xerox, Microsoft, and most recently Meta, where he was part of the Fundamental AI Research unit (FAIR). His current focus is on making AI models safer, more reliable, and more capable by deepening our understanding of how they work. Throughout his career he contributed advances in Machine Learning, Statistical Machine Translation, and Natural Language Processing, and led the transfer of research results to large-scale production environments. His work has been published and received awards at major international conferences, and he is co-inventor of 25 US Patents.

Dr Yali Du is a Senior Lecturer in AI at King’s College London, and a Turing Fellow at The Alan Turing Institute. She is the Head of the Distributed AI Group and leads the Cooperative AI Lab. Her research focuses on building cooperative and safe AI agents that can learn, coordinate, and align with humans in intelligent decision-making tasks, spanning multi-agent cooperation, human–AI coordination, and value alignment. She received the AAAI New Faculty Highlights Award in 2023 and was also named a Rising Star in AI in the same year. She has delivered tutorials on cooperative multi-agent learning at ACML 2022 and AAAI 2023. Dr Du serves as an Associate Editor for the Journal of AAMAS and IEEE Transactions on Artificial Intelligence, and as a Senior Area Chair for AAMAS, as well as an Area Chair for NeurIPS, ICML, ICLR, and IJCAI. She has also served on the organising committees of DAI 2025, NeurIPS 2024, and AAMAS 2023.

Simon Lucas is a full professor of AI in the School of Electronic Engineering and Computer Science at Queen Mary University of London where he leads the Game AI Research Group.  He was previously Head of School of EECS at QMUL.  He recently spent two years as a research scientist / software engineer in the Simulation-Based Testing team at Meta, applying simulation-based AI to automated testing.  

Simon was the founding Editor-in-Chief of the IEEE Transactions on Games and co-founded the IEEE Conference on Games, was VP-Education for the IEEE Computational Intelligence Society and has served in many conference chair roles.  His research is focused on simulation-based AI (e.g. Monte Carlo Tree Search, Rolling Horizon Evolution), bandit-based optimisation, and LLMs.

Volker Tresp is a Professor of Informatics at Ludwig Maximilian University of Munich (LMU). He earned a Diploma in Physics from the University of Göttingen (1984) and M.Sc., M.Phil., and Ph.D. degrees from Yale University (1986–1989), where he worked in the Image Processing and Analysis Group. After joining Siemens in 1990, he led multiple machine learning research teams, becoming Siemens Inventor of the Year in 1997 and the company’s first Distinguished Research Scientist in 2018. He is known for seminal contributions to Bayesian machine learning, Gaussian processes, representation learning for multi-relational graphs, and knowledge graphs. Since 2011 at LMU, he has taught machine learning and led a research group. In 2020, he became an ELLIS Fellow and currently co-directs the ELLIS program on Semantic, Symbolic, and Interpretable Machine Learning.

Aqib Ansari is the Co-Founder and Chief AI Officer at Beam AI, where he leads the development of self-learning AI agent systems for the enterprise. With a background in Robotics, Cognition & Intelligence from the Technical University of Munich (TUM), Aqib brings deep expertise in building autonomous systems that continuously improve through real-world feedback. Under his technical leadership, Beam AI has pioneered closed-loop learning frameworks that have demonstrated up to 40% accuracy improvements and 33% faster execution times across enterprise deployments. Aqib’s work sits at the intersection of applied AI research and production-grade agent systems, with a focus on solving the adoption challenges that prevent AI pilots from reaching production scale.

Michal is the Founding Researcher at a stealth startup, tenured researcher at Inria, and the lecturer at the MVA master of ENS Paris-Saclay. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. That is why he is working on methods and settings that are able to deal with minimal feedback, such as deep reinforcement learning, bandit algorithms, self-supervised learning, or self play. Michal has recently worked on representation learning, word models and deep (reinforcement) learning algorithms that have some theoretical underpinning. In the past he has also worked on sequential algorithms with structured decisions where exploiting the structure leads to provably faster learning. Michal is now working on large large models (LMMs), in particular providing algorithmic solutions for their scalable fine-tuning and alignment. He received his Ph.D. in 2011 from the University of Pittsburgh, before getting a tenure at Inria in 2012 and starting Google DeepMind Paris in 2018 with Rémi Munos, In 2024, he became the principal Llama engineer at Meta, building online reinforcement learning stack and research for Llama 3.

Christian P Robert is professor at Université Paris Dauphine since 2000, and part-time professor at University of Warwick since 2013. He is a Fellow of the American Statistical Association (2012) and the Institute of Mathematical Statistics (1996), a former editor of the Journal of the Royal Statistical Society (2006-2010) and deputy editor of Biometrika (2018-2023). He was head of the Statistics Laboratory of CREST-ENSAE from 1992 till 2010. He was nominated a senior member of Institut Universitaire de France in 2010. Christian Robert is a specialist of Bayesian inference and computational statistics. He has been working for about fifteen years on approximate Bayesian inference methods, induced by the complexity or size of the data. His results validate Monte Carlo methods on generative models and help in the construction of efficient dimensionality reduction techniques.