ICCS is well known for its lineup of keynote speakers.
This page will be update frequently as names and lecture details become available.
Andrew Adamatzky
University of the West of England Bristol
UK
George Karniadakis
Brown University
USA
PINNs and Deep Neural Operators for Building Digital Twins
Amanda Randles
Duke University
USA
Christian Schroer
DESY | University of Hamburg
Germany
From Photons to Knowledge: Computational Challenges and Opportunities at the Next Generation of X-ray Light Sources
Estela Suarez
Jülich Supercomputing Centre | University of Bonn
Germany
Balancing Energy, Resource Utilization, and Performance in HPC System Operations
University of the West of England Bristol, UK
WEB
Andrew Adamatzky is is Professor of Unconventional Computing and Director of the Unconventional Computing Laboratory in the Department of Computer Science at the University of the West of England, Bristol, UK. His research spans molecular and reaction–diffusion computing, collision-based computation, cellular automata, slime mould computing, massive parallelism, applied mathematics, complexity science, nature-inspired optimisation, collective intelligence, robotics and bionics, computational psychology, non-linear science, and novel hardware for future and emergent computation.
He is the author of seven books, including Reaction-Diffusion Computing, Dynamics of Crow Minds, Physarum Machines, The Silence of Slime Mould, Reaction-Diffusion Automata, Proteinoid Proto-Neural Networks, Weirdware and has edited more than thirty volumes in computing, such as Collision-Based Computing, Game of Life Cellular Automata, Memristor Networks, Fungal Machines, and Post-Apocalyptic Computing. Professor Adamatzky is also Founding Editor-in-Chief of the Journal of Cellular Automata and the Journal of Unconventional Computing, and Editor-in-Chief of Parallel, Emergent and Distributed Systems and Parallel Processing Letters.
TBA
Brown University, USA
WEB 1 | WEB 2
George Karniadakis is from Crete. He is an elected member of the National Academy of Engineering, National Academy of Arts and Sciences, and a Vannevar Bush Faculty Fellow. He received his S.M. and Ph.D. from Massachusetts Institute of Technology (1984/87). He was appointed Lecturer in the Department of Mechanical Engineering at MIT and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continued to be a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018-), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the SES G.I. Taylor medal (2014), the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt award in 2017, the SIAM Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US Association in Computational Mechanics. His h-index is 156 (highest in Applied Mathematics) and he has been cited over 148,000 times.
I will review physics-informed neural networks (PINNs) and summarize new extensions for applications in computational engineering. I will also review new representations of interpretable deep neural operators that take as inputs functions and distributions for system identification and real time inference. I will then present how we can reduce energy requirements by neuromorphic computing and spiking neural networks. Pretrained DeepOnets can serve as foundation models for building digital twins, and I will provide some examples in engineering applications.
TBA
DESY | University of Hamburg, Germany
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Christian Schroer is leading the scientific programme of the synchrotron radiation source PETRA III and is a professor for X-ray nanoscience and X-ray optics at the University of Hamburg. His main field of research is X-ray microscopy and X-ray optics that have wide range of applications in physics, chemistry, the life, materials and geosciences, as well as in nanotechnology. Schroer made his doctoral studies in mathematical physics. After a visit as postdoctoral fellow to the University of Maryland, he worked as a research and teaching associate at RWTH Aachen University in the field of X-ray optics and microscopy. Finishing his habilitation in 2004, he joined DESY in Hamburg as a staff scientist. From 2006 to 2014, he was professor for structural physics of condensed matter at Technische Universität Dresden, before he moved back to Hamburg to take on his current position. As leading scientist of PETRA III, he works on the strategic development of the facility. In particular, he led the development of the science case and the conceptual design of PETRA IV, DESY’s planned ultra-low emittance source. As X-ray microscopist, he is working on DESY’s imaging strategy and is cofounder and speaker of Helmholtz Imaging, a platform of the Helmholtz Incubator on Information and Data Science. His scientific group develops X-ray microscopy for synchrotron radiation sources and X-ray free-electron lasers.
Modern synchrotron radiation facilities are evolving rapidly into highly data-intensive scientific instruments. The experiments at DESY generate vast amounts of high-dimensional data from advanced X-ray imaging, spectroscopy and scattering techniques. This is true of both PETRA III and the upcoming diffraction-limited storage ring PETRA IV. Extracting scientific insight from these measurements requires sophisticated computational pipelines combining large-scale data processing, inverse problem solving and machine learning.
PETRA IV is the ‘Ultimate 4D X-ray microscope for biological, chemical, and physical processes’. Its central mission is to observe the structure, composition, and function of materials under realistic conditions with high spatial resolution while tracking their evolution over time. This requires integrated computational workflows that transform detector data into quantitative physical information.
Integrating experimental infrastructure, large-scale computing, and advanced algorithms across the imaging pipeline is key to addressing these challenges. Many methods involve challenging inverse problems that demand new strategies combining physics-based modelling, optimization, and machine learning. Initiatives such as Helmholtz Imaging foster collaboration between scientists and computer experts.
The transition from PETRA III to PETRA IV will dramatically increase experimental capability and data rates, turning the facility into an inherently computational instrument. Data reconstruction, modelling and analysis will increasingly need to occur close to the experiment, often in real time, to guide measurements and extract meaning from complex datasets. This creates new opportunities for the computational science community in the areas of scalable inverse methods, multimodal data integration, and AI-assisted experimental workflows.
Jülich Supercomputing Centre | University of Bonn, Germany
WEB
Estela Suarez is Joint Lead of the department Novel System Architecture Design at the Jülich Supercomputing Centre, and Associate Professor for High Performance Computing at the University of Bonn. Her expertise is in HPC system architecture and codesign. As leader of the DEEP project series, she has driven the development of the Modular Supercomputing Architecture, including the implementation and validation of hardware, software and applications. In addition, she leads the energy efficiency project SEANERGYS and has led the codesign efforts within the European Processor Initiative in 2018-2024. From 2024 to 2025 she has been Senior Principal Solution Architect at SiPEARL, during a sabbatical. She holds a Master's degree in Astrophysics from the University Complutense of Madrid (Spain) and a PhD in Physics from the University of Geneva (Switzerland).
Operating modern high-performance computing (HPC) systems efficiently requires balancing several competing objectives: minimizing energy consumption, maximizing resource utilization, maintaining high system throughput, and ensuring acceptable response times.
These optimization goals often conflict, making it necessary to adapt system operation according to site-specific priorities and workload characteristics. This talk presents an integrated software approach that combines comprehensive monitoring, AI-driven workload analytics, and dynamic scheduling to improve overall system efficiency.
The SEANERGYS software aims to support production HPC environments while enabling more efficient use of available energy and compute resources. A monitoring infrastructure collects data from hardware and software sensors and correlates them with scheduler information to identify inefficiencies such as underutilized resources. Machine-learning models analyze historical and real-time operational data to characterize workload behavior, predict job resource demands, and identify complementary workloads suitable for co-scheduling. These insights inform dynamic resource management and scheduling policies
that adapt system operation to improve energy efficiency and utilization while maintaining performance targets.