Special Section in the SIAM Journal on Scientific Computing (SISC)
Two Themes: “CSE Software” and “Big Data in CSE”
The Special Section focuses on CSE Software and Big Data in CSE. It will feature high-quality scientific computing papers in either of these two areas (or their intersection).
Interest in software and big data aspects of CSE and scientific computing has been increasing rapidly in the past few years, with many new challenges arising in a variety of application domains. Novel approaches are being developed to tackle these challenges across the entire CSE pipeline, which encompasses the end-to-end process from primary data and abstract models to informed decisions.
In the area of CSE software, specific topics of interest for the Special Section include novel design and development of high-quality software for various aspects of CSE, for example the following:
- CSE software design and development to support advanced parallel algorithms and high-performance computing
- approaches for exploiting new architectural features for scientific simulation, including many-core, accelerators, multithreading, and heterogeneity
- libraries for scientific computing
- scientific software problem solving environments
- approaches to achieving portable performance of numerical codes
- quality assurance of scientific software and reproducibility of results
- innovative implementations of scientific computing and visualization algorithms
The primary focus should be on software and computational methods that have potentially large impact for an important class of scientific or engineering problems.
In the area of big data, specific topics of interest include the following:
- methods for data-driven scientific discovery
- integrating models and massive data to produce better predictions
- large-scale data assimilation
- (Bayesian) inverse problems for data-driven science
- uncertainty quantification with big data
- algorithms and methods for scientific discovery in large-scale experimental or observational data
- sensor networks at massive scale
- numerical and combinatorial algorithms for big data (including randomized algorithms and large-scale optimization)
- data stream algorithms and algorithms and methods for large networks
- fast scalable algorithms for supervised and unsupervised learning (such as nonnegative matrix factorization)
- visual analysis of big data
- software frameworks for large-scale data processing
- efficient implementation of big data algorithms in specialized software or hardware environments
Mitch Chernoff (firstname.lastname@example.org)
SIAM Publications Manager
Brittni Holland (email@example.com)
SIAM Editorial Associate, SISC
Additional relevant information on SISC (including editorial policy and instructions for authors) can be found at http://www.siam.org/journals/sisc.php.