LATEST NEWS
Articles in the Artificial Intelligence Track focus on the development, application, and critical evaluation of AI methods that advance scientific discovery and engineering practice. Articles submitted to this track should demonstrate how AI techniques—including machine learning, deep learning, and hybrid AI‑simulation approaches—transform the way scientific and engineering problems are formulated, understood, and solved. This track highlights integration of AI with computational science and engineering, emphasizing practical impact, methodological innovations, and cross‑disciplinary relevance.
Articles in the Education and Workforce Development Track present innovative and evidence‑based approaches to teaching, learning, and career development in computational science and engineering. Articles submitted to this track should describe case studies, curricular innovations, pedagogical approaches, or community building approaches for either formal or informal learning/training environments. Contributions may address education at all levels, including K12, undergraduate, graduate,, professional, and lifelong learning. These articles will highlight practical approaches, lessons learned, and emerging strategies for preparing learners to effectively develop and apply computational approaches to science and engineering problems as they prepare for and grow in career tracks.
Articles in the Leadership Computing Track describe leadership-class high-performance computing (HPC) resources and their transformative impact on scientific discovery and engineering innovation both today and in the future. Leadership computing systems are indispensable instruments that enable researchers to study humanity’s grandest challenges in science and engineering at a scale and complexity that make them intractable on other systems. Articles in this track focus on how leadership computing resources support a wide range of demanding workloads, including large-scale simulations, data-intensive analysis, artificial intelligence and machine learning, and integrated modeling and experimental workflows.
Articles in the Novel Architectures Track describe the latest developments in computing architectures—including accelerators, heterogeneous systems, specialized processors, and experimental architectures—and their impact on scientific and engineering applications at a level accessible to a broad, interdisciplinary readership. Articles in this track should emphasize real-world use for these architectures and their impact on performance, scalability, energy efficiency, and application design. Articles in this track can also explore innovative uses of existing systems and comparative studies that examine development tools, programming models, and methodologies across architectures. Articles should provide valuable insights into how different platforms support computational science and engineering workloads, and how researchers can adapt to rapidly changing hardware landscapes.
Articles in the Research Software Engineering Track focus on the evolving profession and practice of research software engineering (RSE) in computational science and engineering. Articles in this track can explore the people and the processes that underpin the development of reliable, sustainable, and impactful research software. This track aims to facilitate informed discourse on the skills, career paths, and community structures that support RSEs, as well as the methodologies, tools, and organizational models that enable high-quality, sustainable software engineering in computational research settings.
Track editors: Jeff Carver (University of Alabama) and Karla Morris (Sandia National Laboratories)
As the importance and prevalence of scientific software grows, there’s a corresponding need for appropriate software engineering practices to help ensure long-term sustainability. A growing body of literature demonstrates the interest of scientific developers in advancing the software engineering practices of scientific disciplines. Recognizing that the development of scientific software differs significantly from the development of other software, the goal of this track is to provide a venue for the publication of work at the intersection of science and software engineering. Scientific software includes software that falls into these categories:
We encourage submissions from both the software engineering and science communities. Topics of interest include the following:
Note that this track has two submission types, fully peer-reviewed and editorial reviewed. Papers are expected to make a significant contribution to the literature. Claims should be supported by appropriately rigorous validation. For work in earlier stages or with lighter validation, authors may want to consider the Software Engineering for Science workshops series (http://se4science.org/workshops). Authors should contact the track editors at cise-software@computer.org prior to submission with questions about scope and about the difference between the peer-reviewed and editorial content.
Articles in the Quantum Computing Track focus on the emerging role of quantum computing in advancing scientific discovery and engineering innovation. Articles in this track should use an accessible and application-oriented approach to explore quantum algorithms, systems, and software in the context of real-world scientific and engineering challenges. These articles will highlight progress in quantum hardware, programming models, as well as hybrid quantum–classical approaches, with an emphasis on how such developments can be leveraged by the broader computational scientific and engineering community.
Articles in the Visualization and Data Track focus on the theory, design, management, analysis, and application of methods that enable the understanding, exploration, and communication of complex scientific and engineering data. Articles in this track will emphasize both foundational advances and practical applications, including approaches for data representation, transformation, integration, analysis, and visualization within simulation, experimental, observational, and data-driven workflows. These articles will highlight how data and visual representations transform complex information into insight, supporting discovery, decision-making, reproducibility, and collaboration across computational science and engineering..
When writing your article, please refer to the CiSE-Specific Author Guidelines and the General Author Guidelines. Articles for this track should not exceed 6,000 words, including all main body, bibliography, biography, and table text. Each table and figure counts for 250 words. Please submit electronically through the IEEE Author Portal, selecting this track option.
Contact the track editors at cise-software@computer.org.
