Workshops

Web Address: https://sites.google.com/unicz.it/aixna/

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.

Web Address: https://sites.google.com/view/comp-health-iccs/

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

Web Address: Coming soon.

Contact: Tanzhe Tang, University of Amsterdam, The Netherlands, email

Description: As a successor to its first edition held in Singapore, the 2nd Computational Modeling and Artificial Intelligence for Social Systems (CMAISS) workshop aims to bring together researchers and practitioners working on the development and application of computational methods for studying various social systems, such as cities, traffic, cooperation, collective decision-making, opinion dynamics, and social contagion. These systems are typically stochastic, complex, and often out of equilibrium, requiring innovative methodologies and interdisciplinary perspectives to study. Computational modeling techniques such as agent-based modeling and complex network modeling have proven to be exceptionally powerful for this task, but also face challenges such as validation. Meanwhile, the advances in Artificial Intelligence have attracted growing attention from scholars seeking either to employ AI techniques to study social systems, or to study the social systems in the era of AI. CMAISS seeks to explore opportunities for methodological innovation in computational modeling, AI, and their integration, by fostering interdisciplinary discussions among scientists of network science, game theory, agent-based modeling, machine learning, and large language models. We welcome papers on, but not limited to, the following topics:

• Integration of AI in social system modeling
• Social systems in the era of AI
• Social simulation, multi-agent simulation or agent-based modeling applied to social systems
• Using machine learning and large language models in social simulation methods
• Complex network modeling and analysis
• Modeling evolution of cooperation in social systems
• Modeling opinion dynamics and misinformation
• Modeling collective decision making, aggregation and deliberation

Web Address: http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=190059

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

Web Address: https://ComPsy.computationalscience.nl

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.

Web Address: https://mms.computationalscience.nl/

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.

Web Address: https://sites.google.com/view/rossella-arcucci/home/8th-edition-machine-learning-and-data-assimilation-for-dynamical-systems

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.

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.

Web Address: https://sites.google.com/view/naca-2026/strona-g%C5%82%C3%B3wna

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.

Web Address: https://smartsys.ualg.pt/2026/

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.

Web Address: https://www.hartree.stfc.ac.uk/events/solving-problems-with-uncertainty/

Contact: Vassil Alexandrov, Hartree Centre – STFC, United Kingdom, email

Description: Problems with uncertainty need to be tackled in an increasing variety of areas, from physics, chemistry, engineering, computational biology and environmental sciences to decision making in economics and social sciences. Uncertainty is unavoidable in almost all systems analysis, risk analysis, decision making and modelling and simulation. How uncertainty is handled and quantified shapes the integrity of the analysis, and the correctness and credibility of the solution and the results is especially important when tackling complex systems.
With exascale computing now being a reality, and the advances of big data analytics and scalable AI, larger and larger problems – often requiring hybrid approaches – have to be solved in a systematic way at scale. The problem of solving such problems with uncertainties and quantifying the uncertainties becomes even more important, especially in the case of complex systems, due to the variety and scale of uncertainties in such problems.
The focus of the workshop will be on methods and algorithms for solving problems with uncertainties, stochastic methods and algorithms for solving problems with uncertainties, AI and hybrid approaches dealing with uncertainties, methods and algorithms for quantifying uncertainties such as dealing with data input and missing data, sensitivity analysis (local and global), dealing with model inadequacy, model validation and averaging, software fault-tolerance and resilience, and more.

Web Address: https://www.hartree.stfc.ac.uk/events/teaching-computational-science-workshop-2/

Contact: Evguenia Alexandrova, Hartree Centre – STFC, United Kingdom, email

Description: In the context of rapidly growing applications off AI and Quantum Computing and the entrance of the exascale computing systems in the technological landscape, computational scientists and research technical personal are faced with the pressure to rapidly update their skills in order to benefit from the novel developments of technology. The challenge in front of educators from academia and training is to assess which are the core competences and how to expose the community to them in the most efficient way.
The focus of this workshop is through innovation in teaching computational science in its various aspects (e.g. modeling and simulation, high-performance and large-data environments) at all levels and, in all contexts to support the researchers in their career development through upskilling. Typical topics include but are not restricted to, innovations in the following areas: course content, curriculum structure, methods of instruction, methods of assessment, tools to aid in teaching or learning, evaluations of alternative approaches, and non-academic training in computational sciences. These may be in the context of formal courses or self- directed learning. They may involve, for example, introductory, service, or more advanced courses; specialist undergraduate or postgraduate topics; professional development; or industry-related short courses.
We welcome submissions directed at issues of current and local importance, as well as topics of international interest. Such topics may include transition from school to university, articulation between vocational and university education, quality management in teaching, development of needed HPC and other computational research skills, teaching people from other cultures, attracting and retaining female students, diversification of the workforce, and flexible learning, how complex technological and research topics can be communicated effectively to diverse audiences to encourage upskilling and their adoption.