Keynote Speakers

RTNS 2026 is pleased to welcome the following keynote speakers, bringing perspectives from both academia and industry on predictability, real-time computing, and the deployment of intelligent systems.

Alessandro Papadopoulos

Professor of Computer Engineering, Technical University of Denmark (DTU) / Professor of Electrical and Computer Engineering, Mälardalen University (MDU), Sweden

Predictability in the Edge-to-Cloud Continuum: When Time Matters

Abstract:

Modern cyber-physical systems increasingly rely on distributed intelligence deployed across devices, edge nodes, fog infrastructure, and the cloud. This computing continuum offers unprecedented flexibility and scalability, but it also challenges one of the central assumptions of real-time systems: that the computational and communication substrate can be characterized with sufficiently stable timing properties. In applications such as autonomous vehicles, industrial automation, and cloud-based control, latency, jitter, resource contention, and missed updates do not merely degrade software performance; they directly affect the behavior of the physical system.

This presentation discusses predictability as a cyber-physical property of edge-to-cloud systems. The talk will argue that predictable cyber-physical intelligence requires moving beyond classical worst-case reasoning alone, toward systems that can understand, expose, and manage timing uncertainty across the computing continuum.

Short Bio:

Alessandro Papadopoulos is Professor of Computer Engineering at the Technical University of Denmark (DTU) and Professor of Electrical and Computer Engineering at Mälardalen University (MDU), Sweden. He serves as Director of the AI@MDU initiative, Vice Director of the Mälardalen University Automation Research Center (MARC), a member of the International Advisory Board of the LUMSA International Research Center for Artificial Intelligence Management, and a member of the IEEE Technical Committee on Real-Time Systems (TCRTS) Executive Committee.

He received his Ph.D. from Politecnico di Milano in 2013 under the supervision of Professor Alberto Leva. In 2014, he joined Lund University as a postdoctoral researcher, working with Karl-Erik Årzén and Martina Maggio on resource allocation for cloud infrastructures and real-time systems. He joined MDU as an assistant professor in 2016 and was promoted to associate professor in 2018 and full professor in 2022.

He has served as Program Chair of MED 2022, ECRTS 2023, ICPE 2025, and SEAMS 2027. He has also held visiting and scientific advisory positions at academic institutions and industrial companies, including the University of Málaga (Spain), the University of Bologna (Italy), ABB (Sweden), and Zero Point Technologies (Sweden).

His research spans the real-time systems, automatic control, software engineering, artificial intelligence, and cyber-physical systems communities. He develops theories, methods, and tools for predictable, adaptive, and resource-aware computing systems operating under uncertainty, with applications in embedded and real-time systems, edge-to-cloud computing, robotics, and industrial automation.

Sergei Chichin

Senior R&D Engineer for Avionics Software, Airbus

From Training to Flight: The Industrial Realities of Embedded AI in Avionics

Abstract

The aerospace industry is increasingly looking to leverage Artificial Intelligence to complement the next generation of flight capabilities. However, taking a Machine Learning (ML) model from an unconstrained training environment and deploying it onto a flying, safety-critical system represents a massive engineering leap. In this keynote, we will explore the industrial realities of Embedded AI from an Airbus perspective, highlighting the friction between modern ML paradigms and the strict, deterministic requirements of aviation.

This presentation will walk through the integration of AI within the Airbus development process, grounded in the emerging ED-324/ARP6983 standard and its "W" development cycle. We will specifically focus on the critical "second V" of this cycle—the deployment phase—where some of the most significant engineering bottlenecks occur.

Attendees will gain insight into the primary challenges of deploying ML inference on hybrid compute platforms, including:

• Embeddability Constraints: Adapting resource-heavy ML models for constrained, high-performance aerospace hardware.
• Predictable Timing: Achieving deterministic execution and worst-case execution time (WCET) guarantees on complex, hybrid architectures.
• Robust Partitioning: Ensuring strict segregation when utilizing modern hardware accelerators.
• Navigating Emerging Standards: Leveraging frameworks and standards such as SONNX for ML pipeline deployment.
• Methods and Tools: Overcoming the complexities of new programming models and adapting to significantly shorter deployment cycles.

By bridging the gap between theoretical ML design and physical hardware deployment, this talk aims to outline open problems and potential future pathways for real-time, safety-critical embedded systems in the avionics industry.

Short Bio

Dr. Sergei Chichin is a Senior R&D Engineer for Avionics Software at Airbus, specializing in high-performance compute platforms for safety-critical real-time systems. With a Ph.D. in Computer Science in the area of combinatorial optimization and more than 10 years of industry experience, his current work focuses on bridging the gap between AI model training and industrial, certifiable avionics deployment. He is a key contributor to related emerging industry standards, including ED-324 and SONNX, with a particular focus on ML inference embeddability, robust hardware partitioning, deterministic execution on hybrid compute platforms, and the Processes, Methods, and Tools (PMT) required for the industrialization of future ML-based avionics applications.

Ahlem Mifdaoui

Professor of Computer Engineering, University of Toulouse/ISAE-Supaéro France

A Journey Towards Predictable Networked Systems: From Isolated Determinism to End-to-End Convergence

Abstract

Predictable networked systems are undergoing a transition from isolated real-time communication proprietary networks to integrated time-sensitive infrastructures extending computation, communication, reliability and security constraints. This keynote examines that evolution through the Time-Sensitive Networking perspective, where deterministic guarantees now depend not only on standards specifications, but also on their practical deployment and resilience under realistic conditions. The talk highlights three connected challenges: tight and scalable predictability analysis methodology; its validity under realistic conditions like clock drift, packet mis-ordering, burstiness spikes and security overheads rather than only in theory; and its practical deployment into application domains such as industrial automation, avionics and automotive. It argues that the current phase of research is defined less by whether networked systems can provide determinism, and more by whether predictable behavior can be maintained under heterogeneity, large-scale and non-ideal real conditions. We also confront the final frontier of end-to-end convergence for next-generation predictable networked systems: bridging localized wired TSN networks with 5G/6G cellular and dynamic thousand-node mesh networks over LEO satellite constellations, where predictability needs to be guaranteed under dynamicity, end-to-end technology integration and harsh security constraints

Short Bio

Ahlem Mifdaoui is a Full Professor in the Department of Complex Systems Engineering (DISC) at the Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-Supaéro) part of University of Toulouse, France, where she has been a faculty member since 2008. She serves as the Head of the SysCo (Systèmes Connectés) research team at DISC and has been awarded in 2025 the prestigious "Palmes Académiques" for her dedication and implication in education. Pr. Mifdaoui received her Engineering degree in Computer Science and Air TraƯic Management from ENAC in 2004, and her Ph.D. from the Institut National Polytechnique de Toulouse (INPT) in 2007. In 2016, she obtained her Habilitation (HDR) from the Université Paul Sabatier, specializing in performance analysis of predictable networked systems. Her research focuses on the design, optimization, and formal verification of real-time communication protocols and standards including AVB/TSN and ARINC 664/AFDX. She is an expert in formal mathematical frameworks to compute guaranteed timing bounds for safety-critical systems. Her work spans diverse cyberphysical systems applications, including avionics, automotive, Industry 4.0, and space. Over her career, she has supervised numerous PHD theses in embedded networks and served as the Head of the "Embedded Systems" Major in the Master of Aerospace Engineering at ISAE-Supaéro during seven years. Strongly rooted in industrial applications, her research features deep collaborations with major aerospace leaders such as Airbus, Thales, and IRT Saint Exupéry. She is also highly active in the real-time systems community, serving as the General Chair of WFCS 2024, a Program Chair for ETFA, WFCS and RAGE, a regular TPC member for RTNS, RTAS and RTSS and an Associate Editor for IEEE Transactions on Industrial Informatics.