Keynotes
We have the pleasure of welcoming our keynote speakers at CompSys'26.
| Tuesday June 16, 10:00 |
| Title: Emerging Industrial AI Systems: Large Industry Models, AI Infrastructure, and Quantum Computing |
| By Andre Luckow (BMW Group IT & Ludwig-Maximilians-Universität München) |
| Abstract: Computational demands in the automotive industry are rising rapidly due to the increasing use of AI across engineering, manufacturing, and business processes, as well as the growing scale of industrial and engineering simulation. In this talk, we present AI applications together with the compute and platform infrastructure required to support them. We focus on Large Industry Models (LIMs), which combine industrial data with domain-adapted foundation models to enable engineering reasoning and process-aware decision-making in industrial applications. While AI is transforming computing, continued scaling faces increasing computational and algorithmic limits. Thus, quantum computing may complement AI in selected domains. We therefore examine emerging quantum and hybrid algorithms for AI, optimization, and simulation, and discuss their integration into future heterogeneous compute architectures. |
| Short bio: Andre Luckow is Head of Innovation and Emerging Technologies at BMW Group IT and lecturer at Ludwig-Maximilians-Universität München. He leads BMW Group IT’s global Tech Office Network, with locations in the United States, China, India, and Germany. His research spans high-performance and distributed computing systems, including HPC and cloud middleware for heterogeneous compute infrastructures. More recently, his work has focused on AI/ML systems at scale and quantum-classical hybrid computing, with contributions to quantum middleware and hybrid algorithms for optimization and simulation. |
| Wednesday June 17, 14:00 |
| Title: Succeeding with Agentic AI through Integration |
| By Misja Heuveling (IBM) |
| Abstract: AI is a hot topic in boardrooms today. Today’s leaders see AI as the next Industrial Revolution, creating new opportunities. While exploring all the new opportunities, MIT reports that 95% of the AI projects never make it to production. During this session you will learn about one of the root causes for the failure of these projects, namely the IT complexity and difficulty to securely connect to the right data and systems. During this session we will explain what Agentic AI is and why AI agents only succeed when they are grounded in trusted, well-governed data and capabilities. Attendees will learn how integration platforms can securely expose enterprise data and business functions as reusable services, enabling AI agents to operate safely, reliably, and at scale. You will learn more about AI and MCP gateways, how these allow AI agents to communicate using the Model Context Protocol (MCP) through the integration platform to enterprise systems in such a way that AI projects can be taken into production in a successful manner. |
| Short bio: As Field CTO for IBMs integration portfolio, Misja Heuveling brings deep expertise in the integration domain, helping organizations navigate complex digital transformation challenges. Passionate about innovation, Misja is a trusted advisor to his customers across Benelux and beyond. Misja and his team partner with customers to architect scalable, future-proof integration strategies that bridge cloud and on-premises environments to accelerate business agility and productivity. His experience and strategic insight empower businesses to unlock the full potential of their data and applications. |
| Thursday June 18, 09:15 |
| Title: Quality-Critical Distributed Computing and Artificial Intelligence for Scientific Research |
| By Zhiming Zhao (UvA) |
| Abstract: Advanced data science and AI technologies are opening new pathways for addressing complex societal and scientific challenges. However, developing effective software systems to harness these opportunities involves significant software and computational hurdles. Integrating emerging technologies into the research lifecycle as a novel problem-solving paradigm requires assembling research software components with varying levels of maturity. Moreover, achieving the system-level performance demanded by quality-critical applications, such as Digital Twins, real-time simulations and predictive decision-making, necessitates end-to-end optimization spanning design, deployment, and runtime adaptation. AI technologies have attracted considerable attention for their role in developing algorithms tailored to such quality-critical scientific applications. In this talk, we explore AI-based methods for quality-critical programming, scheduling, and adaptation within a software framework that operates across multiple service layers, bridging both development and operations (DevOps). We also discuss the challenges that quality-critical computing research faces in supporting large-scale AI and Digital Twin applications across distributed infrastructures. |
| Short bio: Dr. Zhiming Zhao is an Associate Professor and leader of the Multiscale Networked Systems (MNS) research group at the Informatics Institute, University of Amsterdam (UvA). His research focuses on developing innovative programming and control models for quality-critical systems on programmable infrastructures — including Clouds, Edges, and Software-Defined Networks — leveraging optimization and artificial intelligence technologies. Supported by a range of EU and Dutch research projects, his team develops Digital Twinning solutions, Virtual Research Environments, and cloud automation tools to tackle data and computational challenges in both industrial innovation and scientific research. Dr. Zhao is a Senior Member of IEEE and serves as Managing Editor of the Journal of Cloud Computing. |
