Workshops

ICCS is co-organised with the Workshops on Computational Science (WCS), a set of thematic workshops organized by experts in a particular area of Computational Science. These workshops are intended to provide a forum for the discussion of novel and more focused topics in the field of Computational Science among an international group of researchers.

If you are interested in organizing a workshop at ICCS 2025, you can find all necessary details on the Call for Workshops webpage.

The list of accepted workshops is available below. Please click through for each workshop’s scope, dedicated web address, and chair contact details.
We will be adding many more workshops over the coming weeks.

  1. Artificial Intelligence for Network Analysis in Biology and Beyond – AI4NetBio
  2. Computational Health – CompHealth
  3. Computational Optimization, Modelling and Simulation – COMS
  4. Computational Psychology and Mental Health – ComPsy
  5. Credible Multiscale Modelling and Simulation – MMS
  6. Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
  7. Navigating Trustworthy and Autonomous Modelling of Complex Systems: AI Meets Computational Science – TAMCS
  8. Numerical Algorithms and Computer Arithmetic for Computational Science – NACA
  9. Smart Systems: Bringing Together Computer Vision, Sensor Networks and Artificial Intelligence – SmartSys

Artificial Intelligence for Network Analysis in Biology and Beyond – AI4NetBio

Web Address: TBA
Contact: Marianna Milano, University Magna Graecia of Catanzaro, Italy, email
Description: The AI4NetBio Workshop – Artificial Intelligence for Network Analysis in Biology and Beyond aims to bring together researchers and practitioners working at the intersection of AI methods and network analysis, with a special focus on bioinformatics and biomedical applications.
We welcome contributions that present novel algorithms, models, and applications of AI to understand, model, and analyze complex networks across multiple domains. Both theoretical advances and practical implementations are encouraged.
Topics of interest include, but are not limited to:
  • AI Methods for Network Analysis:
    Machine and deep learning for network data; graph neural networks (GNNs); reinforcement learning in dynamic networks; probabilistic and evolutionary models; uncertainty quantification.
  • Network Analysis in Bioinformatics:
    Modeling of protein–protein interaction, gene regulatory and metabolic networks; disease–gene association studies; network-based approaches for drug discovery and repurposing.
  • Network Geometry & Representation Learning:
    Hyperbolic embeddings; geometric deep learning; applications in biological and social networks.
  • Complex Network Theory and Applications:
    Community detection, link prediction, influence propagation; dynamics on and of networks (diffusion, epidemics); multilayer and temporal networks.
  • Scalable AI for Large Networks:
    Parallel and distributed AI techniques; handling high-dimensional, sparse, or noisy data.
  • Applications in Real-World Domains:
    Bioinformatics, neuroscience, and healthcare; network-based AI in infrastructure, communication, and transportation systems; AI for social media and recommendation networks.

Why attend?
Network science has become a cornerstone for studying complex biological, biomedical, and clinical systems. Combined with AI, it enables integrative modeling from genomics to connectomics, supports drug repurposing, and enhances computational medicine through interpretable and causal machine learning.
The workshop aims to foster interdisciplinary dialogue between the communities of AI in healthcare and network/pathway analysis, promoting transparency, trust, and innovation in computational biology and medicine.
We invite the submission of original research papers, case studies, and tool presentations on these topics.

Computational Health – CompHealth

Contact: Sergey Kovalchuk, Independent Researcher, Russian Federation, email
Description: The field of computational science application in healthcare and medicine (H&M) is rapidly growing. Modeling and simulation, data and process mining, numerical methods, intelligent technologies provide new insights, support decision making, policy elaboration, etc. Moreover, this area gives quantitative support to emerging concepts in the area like P4-medicine (personalized, predictive, preventive, and participatory), value-based healthcare, and others. This workshop is aimed to bring together research in computational science and intelligent technologies applied in H&M in all the diversity of scales and aspects. Key topics include (but not limited to):
  • Simulation and modeling in healthcare and medicine (H&M)
  • Complex processes and systems in H&M
  • Networks in H&M
  • Uncertainty management in H&M
  • Numerical methods in H&M
  • Data & process mining, ML & AI in H&M
  • Knowledge and data processing in H&M
  • Decision support and recommending systems in H&M
  • Advanced medical information systems

Computational Optimization, Modelling and Simulation – COMS

Contact: Xin-She Yang, Middlesex University London, United Kingdom, email
Description: The 17th workshop “Computational Optimization, Modelling and Simulation (COMS 2026)” will be a part of the International Conference on Computational Science (ICCS 2026) and Workshops on Computational Science (WCS 2026). This will be the 17th event of the COMS workshop series with the first held during ICCS 2010 in Amsterdam, then within ICCS in Singapore, USA, Spain, Australia, Iceland, USA, Switzerland, China, Portugal, Netherlands, Poland, UK, Czech, Spain and Singapore. COMS 2026 intends to provide a forum and foster discussion on the cross-disciplinary research and development in computational optimization, computer modelling and simulation. Accepted papers will be published in Springer’s LNCS Series.
COMS2026 will focus on new algorithms and methods, new trends, and latest developments in computational optimization, modelling and simulation as well as applications in science, engineering and industry.
Topics include (but not limited to):
  • Computational optimization, engineering optimization and design
  • Bio-inspired computing and algorithms
  • Metaheuristics (ant and bee algorithms, cuckoo search, firefly algorithm, genetic algorithms, PSO, simulated annealing etc)
  • Simulation-driven design and optimization of computationally expensive objectives
  • Surrogate- and knowledge-based optimization algorithms
  • Scheduling and network optimization
  • Integrated approach to optimization and simulation
  • Multiobjective optimization
  • New optimization algorithms, modelling techniques related to optimization
  • Design of experiments
  • Application case studies in engineering and industry

Computational Psychology and Mental Health – ComPsy

Contact:
Valeria Epelbaum, University of Amsterdam, The Netherlands, email
Sophie Engels, Northeastern University, USA, email
Description: The workshop aims to advance interdisciplinary research, leveraging computational methods to understand the mechanisms underlying psychological phenomena, predict and improve mental health. It focuses on the development and application of data-driven and knowledge-driven multiscale models, agent-based simulations, and network approaches for psychological dynamics and urban mental health. By integrating computational models and advanced AI techniques, the workshop fosters novel strategies for modelling symptom networks and the influence of urbanity and social connections on mental health. Uncertainty quantification and digital phenotyping are also emphasized to improve prediction accuracy and effective intervention policies.

Credible Multiscale Modelling and Simulation – MMS

Contact: Diana Suleimenova, Brunel University London, United Kingdom, email
Description: Credible modelling and simulation of multiscale systems constitutes a grand challenge in computational science, and is widely applied in fields ranging from the physical sciences and engineering to the life science and the socio-economic domain. Most of the real-life systems encompass interactions within and between a wide range of spatio-temporal scales, and/or on many separate levels of organization. They require the development of sophisticated models and computational techniques to accurately simulate the diversity and complexity of multiscale problems, and to effectively capture the wide range of relevant phenomena within these simulations. Moreover, these multiscale models frequently need large scale computing capabilities as well as dedicated software and services that enable the exploitation of existing and evolving computational ecosystems.
This MMS workshop aims to provide a forum for multiscale application modellers, framework developers and experts from the distributed infrastructure communities to identify and discuss challenges in, and possible solutions for, modelling and simulating multiscale systems. This includes their execution on advanced computational resources, their validation against experimental data and more widely, the efforts required to make these approaches credible for real-world use.

Machine Learning and Data Assimilation for Dynamical Systems – MLDADS

Contact: Rossella Arcucci, Imperial College London, United Kingdom, email
Description: The primary aim of this workshop is to convene researchers from data assimilation, machine learning and dynamical systems to bridge the gaps between these fields. By exploring how these complementary disciplines can accelerate research outcomes and impact, the workshop seeks to foster collaboration, share cutting-edge advancements, and tackle the computational challenges that have limited the application of data assimilation and fusion in high-dimensional complex systems as well as the application of machine learning for dynamical systems.
The MLDADS workshop brings together contributions from the fields data assimilation, machine learning and dynamical systems to fill the gap between these theories in the following directions:
  1. Machine Learning for Dynamical Systems: how to analyse dynamical systems on the basis of observed data rather than attempt to study them analytically; explore the role of generative models.
  2. Data Assimilation for Machine Learning and/or Dynamical Systems: how well does the model under consideration (Machine Learning model and/or Dynamical System) represent the physical phenomena.
  3. Machine Learning for Data Assimilation: how machine learning techniques—such as deep learning, reinforcement learning, and transfer learning—can improve the efficiency and scalability of data assimilation in dynamical system modelling.
  4. Data Assimilation and Machine Learning for Dynamical Systems: how can tools from the interaction between the theories of Data Assimilation and Machine Learning be used to improve the accuracy of the prediction of dynamical systems.

Navigating Trustworthy and Autonomous Modelling of Complex Systems: AI Meets Computational Science – TAMCS

Web Address: TBA
Contact: Yani Xue, Brunel University London, UK, email
Description: The workshop on “Navigating Trustworthy and Autonomous Modelling of Complex Systems: AI Meets Computational Science (TAMCS)” addresses the emerging need for reliable, transparent, and adaptive modelling frameworks for complex systems. With the growing integration of AI into simulation of energy networks, transportation systems, climate and environmental systems, new computational challenges arise around model credibility, autonomous decision-making, and scalable performance.
This workshop aims to bring together researchers from AI, optimisation, computational science, and engineering to discuss how to enhance the accuracy, efficiency, and robustness of complex-system simulations while upholding trustworthiness and autonomy. We invite submissions on advances, methods, and applications related to trustworthiness, autonomy, and computation in complex-system simulation, including but not limited to:
  1. Trustworthy AI for simulations, focusing on scalable and high-dimensional uncertainty quantification, model verification, and explainability to ensure reliable and interpretable AI-assisted frameworks.
  2. Agentic AI for large-scale, multi-agent simulations, from practical use cases such as AI-assisted simulation setup and automated data preparation, to advanced methods for autonomous goal-setting, adaptive multi-objective decision-making, and self-reflective learning.
  3. Scalable, high-fidelity simulations with multi-objective optimisation and network methods (including network dynamics) to enhance adaptive, resilient, and robust behaviour in complex systems.
  4. Applications and case studies demonstrating AI-driven simulations that improve predictive accuracy, computational efficiency, and system robustness.

Numerical Algorithms and Computer Arithmetic for Computational Science – NACA

Contact: Pawel Gepner, Warsaw Technical University, Poland, email
Description: The Workshop on Numerical Algorithms and Computer Arithmetic for Computational Science 2026 (NACA 2026) welcomes submissions that showcase advancements in numerical algorithms and computer arithmetic across various fields of computational science. This includes modelling and simulation, as well as high-performance computing and data-intensive algorithms for scientific applications. While submissions can explore a wide array of topics, the following areas are particularly of interests:
  • Foundations of computer arithmetic: emerging number systems and their applications
  • Novel arithmetic algorithms, their analysis, and applications
  • Efficient, high-performance, novel implementations of computer arithmetic in software and hardware
  • Integer or floating-point operations, elementary and special functions, multiple-precision computing, interval arithmetic
  • New algorithms and properties of floating-point arithmetic in emerging domains and applications
Computer arithmetic has always been at the core of the digital age and is currently driving innovation in domains such as artificial intelligence, high-performance computing, signal processing and security. Scientists across diverse fields heavily rely on numerical algorithms that require computer arithmetic awareness to avoid oversolving. There is a specific demand for scalable tools that are highly efficient and achieve high user-productivity while solving large-scale problems on massively parallel systems. This workshop focuses on numerical algorithms and computer arithmetic, giving special attention to the latest scientific trends and challenges related to implementing numerical software libraries. The objective is to bring together researchers from different institutes, enabling the exchange of experiences and fostering research collaborations.

Smart Systems: Bringing Together Computer Vision, Sensor Networks and Artificial Intelligence – SmartSys

Contact: Pedro J. S. Cardoso, University of Algarve & NOVA LINCS, Portugal, email
Description: Smart Systems incorporate sensing, actuation, and intelligent control to analyze, describe, and resolve situations, making decisions based on available data in a predictive or adaptive manner. Designed for computer scientists, mathematicians, and researchers from diverse application areas, SmartSys’26 – 8th edition – brings together pioneering computational methods from distinct research fields including space, physics, chemistry, life sciences, economics, security, engineering, and arts.
This workshop integrates computer vision, sensor networks, artificial intelligence, and data science to solve computational science problems. The workshop also welcomes contributions from related areas such as affective computing, augmented reality, human-computer interaction, user experience, Internet of Things/Everything, energy management systems, smart grids, operational research, evolutionary computation, time-series analysis, and information systems. All submissions must focus on computational science challenges, using smart systems as modeling, simulation, and optimization tools.