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 2027, you can find all necessary details on the Call for Workshops webpage.
The list of accepted workshops will be 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.
- Bioinformatics, Biomedical Computing, Artificial Intelligence and Network Science – BBC-AI
- Computational Modeling and Artificial Intelligence for Social Systems – CMAISS
- Computational Optimization, Modelling and Simulation – COMS
- Econophysics Colloquium – EC
- Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
- Numerical Algorithms and Computer Arithmetic for Computational Science – NACA
- Quantum Computing – QCW
- Smart Systems: Bringing Together Computer Vision, Sensor Networks and Artificial Intelligence – SmartSys
Bioinformatics, Biomedical Computing, Artificial Intelligence and Network Science – BBC-AI
Web Address: https://sites.google.com/unicz.it/bbc-ai/program
Organizers:
Mario Cannataro, University Magna Graecia of Catanzaro, Italy, email
Marianna Milano, University Magna Graecia of Catanzaro, Italy, email
Mario Cannataro, University Magna Graecia of Catanzaro, Italy, email
Marianna Milano, University Magna Graecia of Catanzaro, Italy, email
Description: Emerging technologies in biomedicine and bioinformatics are generating an increasing amount of complex and heterogeneous data. To tackle the growing complexity associated with emerging and future life science challenges, bioinformatics and computational biology researchers need to explore, develop, and apply novel computational concepts, methods, and tools.
The aim of the BBC-AI Workshop is to bring together computer scientists, bioinformaticians, computational biologists, network scientists, and biomedical researchers to discuss emerging and future directions in computational bioinformatics, biomedical computing, artificial intelligence, and complex network analysis.
The workshop focuses on key computational challenges, including advanced computing architectures, algorithm design, data analysis and knowledge discovery, data management and integration, and the integration of quantitative and symbolic knowledge into executable biomedical theories and models.
Particular attention is devoted to recent advances in artificial intelligence, machine learning, deep learning, graph neural networks, geometric deep learning, and network-based computational approaches for understanding, modeling, and analyzing complex biological, biomedical, and interconnected systems.
The workshop also addresses fundamental challenges in the development of scalable, explainable, trustworthy, and efficient computational methods, including interpretability, bias, ethics, dataset shift, data quality, privacy, and security.
The workshop welcomes original research papers, novel computational methodologies, theoretical contributions, software tools, and real-world applications.
Topics of interest include, but are not limited to:
The aim of the BBC-AI Workshop is to bring together computer scientists, bioinformaticians, computational biologists, network scientists, and biomedical researchers to discuss emerging and future directions in computational bioinformatics, biomedical computing, artificial intelligence, and complex network analysis.
The workshop focuses on key computational challenges, including advanced computing architectures, algorithm design, data analysis and knowledge discovery, data management and integration, and the integration of quantitative and symbolic knowledge into executable biomedical theories and models.
Particular attention is devoted to recent advances in artificial intelligence, machine learning, deep learning, graph neural networks, geometric deep learning, and network-based computational approaches for understanding, modeling, and analyzing complex biological, biomedical, and interconnected systems.
The workshop also addresses fundamental challenges in the development of scalable, explainable, trustworthy, and efficient computational methods, including interpretability, bias, ethics, dataset shift, data quality, privacy, and security.
The workshop welcomes original research papers, novel computational methodologies, theoretical contributions, software tools, and real-world applications.
Topics of interest include, but are not limited to:
- Artificial intelligence and data science
- Artificial intelligence, machine learning, and deep learning in bioinformatics and healthcare
- Explainable, trustworthy, and ethical AI for biomedical applications
- Big data analytics, data mining, and knowledge discovery
- Generative AI and foundation models for niomedical data
- Biomedical text mining, natural language processing, and sentiment analysis
- Affective computing and intelligent healthcare applications
- Predictive modeling and decision-support systems
- Network Science and Graph-Based AI
- Graph neural networks and graph representation learning
- Geometric deep learning and hyperbolic network embeddings
- Complex network theory, community detection, and link prediction
- Network alignment, network comparison, and network integration
- Multilayer, heterogeneous, temporal, and dynamic networks
- Network topology, geometry, and higher-order structures
- Network diffusion, influence propagation, and epidemic modeling
- Network-based AI applications in biological, social, and technological systems
- Computational Bioinformatics and Systems Biology
- Computational genomics, transcriptomics, proteomics, and metabolomics
- Multi-omics data integration and analysis
- Protein–protein interaction, gene regulatory, and metabolic networks
- Biological pathway analysis and functional enrichment
- Systems biology and computational modeling of biological processes
- Disease–gene association analysis, biomarker discovery, and drug repurposing
- Integration of quantitative and symbolic knowledge into executable biomedical models
- Biological and biomedical databases, knowledge graphs, and ontologies
- Advanced Computing Architectures and Algorithms
- Advanced algorithm design for bioinformatics and biomedicine
- High-performance computing for computational biology
- Parallel and distributed bioinformatics algorithms
- Cloud computing and scalable computational infrastructures
- Emerging computing architectures and programming models
- Parallel visualization and exploration of omics and clinical data
- Parallel biomedical image and biosignal processing
- Scientific workflows and reproducible computational research
- Collaborative computing environments and distributed services
- Biomedical Computing and Healthcare Applications
- Artificial intelligence for biomedical imaging and clinical data analysis
- Computational neuroscience, brain networks, and connectomics
- Modeling and simulation in healthcare and medicine
- Healthcare data management, integration, quality, privacy, and security
- Internet of Things and intelligent healthcare systems
- Cloud-based health systems and biomedical services
- Applications of AI and network science in infrastructure, communication, and transportation systems
Computational Modeling and Artificial Intelligence for Social Systems – CMAISS
Web Address: Coming soon.
Organizer: Tanzhe Tang, University of Amsterdam, The Netherlands, email
Description: The 3rd Workshop on Computational Modeling and Artificial Intelligence for Social Systems (CMAISS) will be held as part of ICCS 2027 in Krakow, Poland.
CMAISS aims to bring together researchers and practitioners working on the development and application of computational methods for studying a wide range of social systems, including (but not limited to) cities, traffic, cooperation, collective decision-making, opinion dynamics, and social contagion. These systems are typically stochastic, complex, and often out of equilibrium, and therefore require innovative methodologies and interdisciplinary perspectives.
Computational modeling approaches (such as agent-based modeling, multi-agent simulation, and complex network modeling) have proven exceptionally powerful for this purpose, but also raise challenges such as validation, scalability, inference, calibration, and interpretability. Meanwhile, rapid advances in Artificial Intelligence, including machine learning and large language models (LLMs), have attracted growing attention from scholars seeking to employ AI techniques to study social systems, and to understand social systems in an era increasingly shaped by AI.
CMAISS particularly welcomes contributions that bridge computational modeling and AI, fostering interdisciplinary discussions among researchers in network science, game theory, agent-based modeling, machine learning, and large language models.
We welcome submissions on, but not limited to, the following topics:
CMAISS aims to bring together researchers and practitioners working on the development and application of computational methods for studying a wide range of social systems, including (but not limited to) cities, traffic, cooperation, collective decision-making, opinion dynamics, and social contagion. These systems are typically stochastic, complex, and often out of equilibrium, and therefore require innovative methodologies and interdisciplinary perspectives.
Computational modeling approaches (such as agent-based modeling, multi-agent simulation, and complex network modeling) have proven exceptionally powerful for this purpose, but also raise challenges such as validation, scalability, inference, calibration, and interpretability. Meanwhile, rapid advances in Artificial Intelligence, including machine learning and large language models (LLMs), have attracted growing attention from scholars seeking to employ AI techniques to study social systems, and to understand social systems in an era increasingly shaped by AI.
CMAISS particularly welcomes contributions that bridge computational modeling and AI, fostering interdisciplinary discussions among researchers in network science, game theory, agent-based modeling, machine learning, and large language models.
We welcome submissions 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 the evolution of cooperation in social systems
- Modeling opinion dynamics and misinformation
- Modeling collective decision making, aggregation and deliberation
Computational Optimization, Modelling and Simulation – COMS
Web Address: https://coms2027.carrd.co/
Organizer: Xin-She Yang, Middlesex University London, United Kingdom, email
Description: The 18th workshop “Computational Optimization, Modelling and Simulation (COMS 2027)” will be a part of the International Conference on Computational Science (ICCS 2027) and Workshops on Computational Science (WCS 2027). This will be the 18th 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, Singapore and Germany. COMS 2027 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.
COMS2027 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):
COMS2027 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
Econophysics Colloquium – EC
Web Address: https://www.econophysics-colloquium.org
Organizers:
Tiziana Di Matteo, Kings College London, UK, email
Giuseppe Brandi, Northeastern University London, UK, email
Tiziana Di Matteo, Kings College London, UK, email
Giuseppe Brandi, Northeastern University London, UK, email
Description: The Econophysics Colloquium (EC 2027) is the 13th edition of the annual international meeting dedicated to econophysics, a field that applies methods from statistical physics and complex systems to the study of financial and economic phenomena.
Held within the framework of the International Conference on Computational Science, the colloquium brings together physicists, economists, data scientists, and practitioners to discuss quantitative approaches to economic and financial systems.
Topics include statistical methods, modelling of financial markets, agent-based simulations, complex networks, and computational analysis of economic data.
EC 2027 provides a forum for interdisciplinary exchange, highlighting how computational and data-driven methods can improve our understanding of economic dynamics and financial stability.
Held within the framework of the International Conference on Computational Science, the colloquium brings together physicists, economists, data scientists, and practitioners to discuss quantitative approaches to economic and financial systems.
Topics include statistical methods, modelling of financial markets, agent-based simulations, complex networks, and computational analysis of economic data.
EC 2027 provides a forum for interdisciplinary exchange, highlighting how computational and data-driven methods can improve our understanding of economic dynamics and financial stability.
Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
Organizers: Rossella Arcucci, Imperial College London, United Kingdom, email
Description: Data assimilation and data fusion are entering a new phase driven by rapid advances in Machine Learning and AI. Emerging approaches (including generative AI, foundation models, learned surrogates, and probabilistic methods) are opening new possibilities for integrating heterogeneous observations with complex models, accelerating inference, representing uncertainty, and operating effectively in high-dimensional settings. The MLDADS workshop aims to bring together researchers developing next-generation methods at the intersection of data assimilation, machine learning, and AI, with a focus on scalable, robust, and scientifically grounded approaches for challenging real-world applications.
Numerical Algorithms and Computer Arithmetic for Computational Science – NACA
Organizer: Pawel Gepner, Warsaw University of Technology, Poland, email
Description: The Workshop on Numerical Algorithms and Computer Arithmetic for Computational Science 2027 (NACA 2027) 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 interest:
- 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
Quantum Computing – QCW
Web Address: https://qcw2027.agh.edu.pl/
Organizer: Karol Capala, AGH University of Krakow, Poland, email
Description: Quantum computing is a paradigm that exploits the fundamental principles of quantum mechanics to solve problems in various fields of science that are beyond the possibilities of classical computing infrastructures. Despite the increasing activity in both theoretical research and hardware implementations, reaching the state of useful quantum supremacy is still an open question. This workshop aims to provide a forum for computational scientists, software developers, computer scientists, physicists and quantum hardware providers to understand and discuss research on current applications of quantum informatics to the field of computational science.
Smart Systems: Bringing Together Computer Vision, Sensor Networks and Artificial Intelligence – SmartSys
Web Address: https://smartsys.ualg.pt/2027/
Organizer: Pedro J. S. Cardoso, University of Algarve, 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’27 – 9th 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 track 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.