Keynote Speakers

Valentin Robu

CWI & TUE

Valentin Robu is a senior scientist at CWI, the Netherlands National Center for Mathematics and Computer Science in Amsterdam, part-time Full Professor in AI for Decentralised Energy systems at TU Eindhoven, and visiting research collaborator in the ECE department at Princeton. His background and core expertise are in distributed AI, multi-agent systems, computational game theory and their application in enabling the next-generation energy systems. He has published 200+ papers in top conferences and journals in both CS and EE, and has been investigator in several large, national energy re-lated projects, both in the Netherlands, the UK, and the EU. He has won a number of awards, such as the 2019 UK Innovation of the Year Award (awarded by the IET, the UK's Institute for Engineering and Technology), for his work with Scottish Power Energy Networks.

Keynote Title: Enabling Decentralized Energy Systems Using Artificial Intelligence

Abstract: Our energy systems are becoming increasingly decentralized, with many autonomous interacting parties e.g. prosumers, electric vehicles, distributed energy resources. At the same time, they are increasingly required to deal with new challenges, such as intermittent renewable generation and new demands, such as from power-hungry AI computations. This talk will provide an overview for how techniques from multi-agent systems (MAS), distributed AI and algorithmic game theory (AGT) can help us model and address these challenges. The talk will cover the use of MAS and AGT methods in a number of energy scenarios, ranging from the charging of self-interested electric vehicles, such that the charging capacity of distribution networks is not exceeded, or routing EVs at distributed charging points to minimize queuing times. The talk will also touch on some other scenarios where incentive design and distributed artificial intelligence play an increasing key role, such as distributed demand-side response, virtual power plant formation, or peer-peer trading and fair sharing of resources in energy communities.

Wil van der Aalst

RWTH Aachen University & Celonis

Prof. dr. ir. Wil van der Aalst is a full professor at RWTH Aachen University, where he leads the Process and Data Science (PADS) group. He is also Chief Scientist at Celonis, affiliated with the Fraunhofer Institute for Applied Information Technology (FIT), and Deputy CEO of the Internet of Production (IoP) Cluster of Excellence. Additionally, he co-directs the RWTH Center for Artificial Intelligence. His research covers a broad spectrum, including process mining, business process management, Petri nets, simulation, workflow automation, and model-based process analysis. With an H-index of 187 and over 162,000 citations according to Google Scholar, he is one of the most cited computer scientists globally. According to Research.com, he is the highest-ranked computer scientist in Germany and ranked 8th worldwide. He has contributed significantly to both academia and industry, shaping the development of process mining tools and standards. Van der Aalst has received honorary degrees from several universities and holds fellowships from IFIP, IEEE, and ACM. He is a member of multiple academies, including the Royal Netherlands Academy of Arts and Sciences and the German Academy of Science and Engineering. In 2018, he received the prestigious Alexander von Humboldt Professorship.

Keynote Title: Towards True Process Intelligence: Leveraging Object-Centric Process Mining Beyond GenAI

Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities, from summarizing meetings and generating reports to assisting with code development. The broader advancements in generative, predictive, and prescriptive AI are reshaping how organizations approach information work. However, despite these technical breakthroughs, many operational problems persist: delays, missed deadlines, and inefficiencies remain widespread. The root cause lies not in the limitations of AI itself but in the underlying processes that remain opaque, fragmented, or poorly managed. In this talk, Wil van der Aalst argues that the absence of process awareness is a critical barrier to realizing the full potential of AI in organizational settings. AI systems are often applied in isolation, without a clear understanding of the end-to-end processes they are intended to support. He will demonstrate how object-centric process mining provides the missing link, offering visibility into real process flows and enabling more meaningful integration of AI. Using recent experiments, including applications of LLMs in process management scenarios, he will highlight where these technologies succeed, where they fail, and what this implies for the future of predictive and prescriptive AI. A deeper understanding of the processes is essential to avoid superficial solutions and ensure that AI contributes to measurable operational improvements.

Marten van Dijk

CWI

Marten van Dijk, IEEE Fellow, is group leader and founder of the Computer Security group at CWI, the Netherlands, with over 20 years of experience in both industry (Philips Research and RSA Laboratories) and academia (MIT, University of Connecticut, Vrije Universiteit van Amsterdam). His work has been recognized by the IEEE CS Edward J. McCluskey Technical Achievement Award 2023, the A. Richard Newton Technical Impact Award in Electronic Design Automation 2015, and has received several best and test-of-time paper awards, see also https://www.cwi.nl/en/people/marten-van-dijk/. He is known for his work on secure computation, in particular, the AEGIS processor -- the first single-chip secure processor, Physical Unclonable Functions (PUFs), Fully Homomorphic Encryption over the Integers, and Oblivious RAM. He is now actively researching the intersecting field of security and machine learning.

Keynote Title: Can we protect our private data in the Machine Learning age?

Abstract: Protecting data for training machine learning models comes at a significant cost. We shortly explain how secure processor architectures and secure multi-party computation can protect the confidentiality of computations that lead to a final global machine learning model. We explain in more detail how we can add a differential privacy mechanism to limit privacy leakage from the outputted/queryable final global model. We demonstrate that no meaningful differential privacy guarantee can be obtained together with practical utility (test accuracy). The recent introduction of PAC Privacy for instance-based security may be the needed paradigm shift.