Astroinformatics is an interdisciplinary field of study involving the combination of astronomy, data science, machine learning, informatics, and information / communications technologies. [2][3] The field is closely related to astrostatistics.

Astroinformatics is the community’s response to the challenges and opportunities given by the exponential growth of data and computing capabilities. It is the emerging methodology for the data-intensive, computationally enabled Astronomy in the 21 st century.

Welcome to the International AstroInformatics Association (IAIA) - a professional, non-profit organization intended to support and advance the growing field of Astroinformatics.

My research interests encompass a wide range of areas across scientific AI (including physics-enhanced AI, geometric AI, statistical AI, generative AI), astrostatistics, Bayesian inference, harmonic analysis, optimisation, and computational techniques.

Astroinformatics includes a broad spectrum of informatics specialties including data-to-knowledge transformations, semantic data integration, information visualization, knowledge extraction, sky-based and catalog-based indexing techniques, information retrieval methods, data mining and knowledge discovery methods, content-based and context ...

Our goal is to develop new methods and tools to deal with the exponentially increasing amount of data in astronomy. In the last two decades, computers have revolutionized astronomy. Advances in technology have led to the advent of new detectors, complex instruments, and innovative telescope designs.

Jan 1, 2009 · Astroinformatics includes a set of naturally-related specialties including data organization, data description, astronomical classification taxonomies, astronomical concept ontologies, data...

Jan 8, 2023 · Astroinformatics is primarily focused on developing the tools, methods, and applications of computational science, data science, machine learning, and statistics for research and education in data-oriented astronomy.

Key topics at Astroinformatics 2024 include: machine learning and advanced statistical methods, data challenges from ongoing and future large surveys and projects, challenges of peta-scale surveys and beyond, methodology transfer, inter, intra, and trans disciplinarity, reproducibility and robustness, foundation models and generative AI.

The Astroinformatics and Astrostatistics section covers topics, original research and peer-reviewed articles related to the latest research and development in the field of Astroinformatics and Astrostatistics.