Add a Research Group Link *******

ADAMIS *******

The ADAMIS team is a transverse cross-disciplinary team of the Universite de Paris Laboratoire AstroParticule et Cosmologie at the interface between (astro)physics, mathematics, statistical information processing, and scientific computing. It aims at developing, and applying to frontier physical problems, innovative techniques in data analysis, physical modeling, and numerical simulation.

AstrIRG: Astroinformatics Research Group

We are a young focus group, a group of astrophysicists and computer scientists, exploring the efficacy of data science methods, developed in tandem with their proper physical interpretations, to address large scale problems in astronomy. working on exploring the efficacy of data analytic methods in solving problems in astronomy. Our initiative is supported by the Bangalore Section of IEEE Computer Society.

Astrostatistics in France *******

This website is the portal of the french community around astrostatistics to exchange news, advices, methods, links, and foster interdisciplinary collaborations.

Astrophysics Source Code Library

The Astrophysics Source Code Library (ASCL) is a free, on-line registry for source codes of interest to astronomers and astrophysicists, and lists codes which have been used in research that has appeared in, or been submitted to, peer-reviewed publications. ASCL entries are indexed by the SAO/NASA Astrophysics Data System (ADS).

Bayes Forum

Discussion group on data analysis based on Bayesian methods/information theory at the Max Planck Institutes for Extraterrestrial Physics and Astrophysics in Garching (Germany).

Berkeley Center for Time Domain Informatics *******

CTDI is a cross-discipinary collaboration at the University of California Berkeley devoted to the emerging discipline of time-domain astronomy and informatics. We are interested in extracting optimal and novel information the huge landscape of variable stars and transient events in the Universe using machine learning techniques applied to wide-field multi-epoch visible light surveys of the sky. For projects generating thousands of gigabytes of new data a night (such as the proposed Large Synoptic Survey Telescope), we generate calibrated probabilistic statements about the physical nature of astronomical events. Uncovering anomalous events that do not fit easily into a currently accepted classification taxonomy – events that may lead to completely new scientific discoveries – is particularly emphasized in our work.

California Harvard Astrostatistics Collaboration

Founded in 1997, the California-Harvard Astrostatistics Collaboration (CHASC) is a collaboration of statisticians and astronomers at Harvard University, the Harvard Smithsonian Center for Astrophysics, Imperial College London, and the Universities of California at Irvine and Davis. The reseraches in CHASC develop statistical methods, computational techniques, and freely available software to address outstanding inferential problems in high-energy astrophysics and in solar physics.

CosmoStat Laboratory

This organization conducts advanced methodological research on statistics and signal processing for cosmology. Methods include compressed sensing and sparsity, blind source separation, inpainting and deconvolution. They are applied to the cosmic microwave background, weak lensing and galaxy clustering datasets. The Laboratory is operated by the Service d’Astrophysique of the Centre d’Etudes Atomique de Saclay FR.

David van Dyk

Professor van Dyk is a statistician at Imperial College who develops statistical techniques for astronomy. His work focuses on new Bayesian methods for high-energy astrophysics, stellar evolution, solar physics, and cosmology.

IIA-PennState Astrostatistics Schools

The IIA-PennState astrostatistics schools are designed for all levels of practicing astrophysicists wishing to enhance their grasp of the application of statistics to their research. In addition to lectures, there is a heavy emphasis on hands-on tutorials using software tools implemented in the public-domain multi-platform R computing environment, which, besides being open-source, is the current standard in research-level statistical computation. The lecture notes tutorials of the previous schools (2007, 2008, 2010) are on-line. Opportunities for participants to brainstorm with the faculty of the school on their research problems which involves the application of statistical methods, and discussions of papers from the astrophysics literature are part of the schedule. The schools are organised by Prajval Shastri & Sabyasachi Chatterjee from IIA, and Jogesh Babu from PennState.

Information Field Theory

Resource web page for people working on information field theory (IFT). IFT deals with Bayesian inference of fields.

Penn State University, Center for Astrostatistics

Provides resources for astrostatistics, operates the Summer School in Statistics for Astronomers, organizes SCMA meetings, operates the VOStat Rev 2 Web service, authors textbooks, provides resources for the R statistical software system.

University of Leicester, Simon Vaughan

Research interests include X-ray astronomy, time series analysis in astronomy, and Bayesian methods. Links to IDL routines and other resources.

Vanderbilt Initiative in Data-intensive Astrophysics (VIDA) Astroinformatics Portal

This Web site includes a growing set of tools for rapid data visualization, quick-look analysis, data sharing, and end-to-end automated processing and classification of time-series data. Tools currently include Filtergraph, LCanimator, LCchopper, LCsimulator, EBfactory, and others.

Research Group Links *******

This page provides links to individual researchers and research groups in astrostatistics and astroinformatics. Members are invited to add their group.

Victor Pankratius – Astroinformatics @ MIT

Victor Pankratius – Astroinformatics @ MIT Haystack

High Performance Intelligent Decision Systems – HighPIDS

Our research group works on design and implementation of decision support systems based on intelligent algorithms to work on high performance computer architectures. The goal of such algorithms is to solve semi-structured data mining and optimization problems. The researchers involved in this project, which are experts in complementary fields, intend to work together to combine their research to advance and contribute to the field of decision support systems. Such systems are extremely important to organizations as they provide valuable information that helps managers take strategic decisions. Currently, the HighPIDS research group works with machine learning algorithms applied to space weather forecasting.