The Future of Scientific Knowledge Discovery in Open Networked Environments
http://sites.nationalacademies.org/PGA/brdi/PGA_060422

New York Workshop on Computer, Earth, and Space Sciences 2011
http://www.giss.nasa.gov/meetings/cess2011/

Innovations in Data-Intensive Astronomy
http://www.nrao.edu/meetings/bigdata/

Astrostatistics and Data Mining in Large Astronomical Databases
http://www.iwinac.uned.es/Astrostatistics/

Statistical Challenges in Modern Astronomy V (including summer school & tutorials)
http://astrostatistics.psu.edu/su11scma5/

Very Wide Field Surveys in the Light of Astro2010
http://widefield2011.pha.jhu.edu/

Statistical Methods for Very Large Datasets
http://www.regonline.com/builder/site/Default.aspx?eventid=757633

23rd Scientific and Statistical Database Management Conference
http://ssdbm2011.ssdbm.org/

International Statistical Institute (ISI) World Congress
http://www.isi2011.ie/

NASA Conference on Intelligent Data Understanding
https://c3.ndc.nasa.gov/dashlink/projects/43/

1. Summer School in Statistics for Astronomers VII (June 6-10, 2011)
2. Pre-conference Tutorials (June 11-12, 2011)
3. Statistical Challenges in Modern Astronomy V (June 13-17, 2011)*

*Web site: http://astrostatistics.psu.edu/su11scma5/

Registration is now open until May 6
(Summer School registration may close earlier if the enrollment limit is reached)

Contributed papers for the SCMA V conference are welcome

Summer School in Statistics for Astronomers: The seventh summer school is an intensive week covering basic statistical inference, several fields of applied statistics, and hands-on experience with the R computing environment. Topics include: exploratory data analysis, hypothesis testing, parameter estimation, regression, bootstrap resampling, model selection & goodness-of-fit, maximum likelihood and Bayesian methods, nonparametrics, spatial processes, and times series. Instructors are mostly faculty members in statistics.

Pre-conference tutorials: Instruction in four areas of astrostatistical interest presented during the weekend between the Summer School and SCMA V conference. Topics are: Bayesian computation and MCMC; data mining; R for astronomers; and wavelets for image analysis. Instructors are members of the SCMA V Scientific Organizing Committee.

SCMA V conference: Held every five years, SCMA conferences are the premier cross-disciplinary forum for research statisticians and astronomers to discuss methodological issues of mutual interest. Session topics include: statistical modeling in astronomy, Bayesian analysis across astronomy; Bayesian cosmology; data mining and informatics; sparsity; interpreting astrophysical simulations; time domain astronomy; spatial and image analysis; and future directions for astrostatistics. Invited lectures will be followed by cross-disciplinary commentaries. The conference welcomes contributed papers from statisticians and astronomers.

Visit http://astrostatistics.psu.edu/su11scma5/ for more information and registration

Contacts:
Eric Feigelson, Dept. of Astronomy & Astrophysics, Penn State, edf@astro.psu.edu
G. Jogesh Babu, Dept. of Statistics, Penn State, babu@stat.psu.edu

URL: http://hea-www.harvard.edu/AstroStat/CAS2010

The California-Boston-Smithsonian Astrostatistics Collaboration plans to host a mini-workshop on Computational Astro-statistics. With the advent of new missions like the Solar Dynamic Observatory (SDO), Panoramic Survey and Rapid Response (Pan-STARRS) and Large Synoptic Survey (LSST), astronomical data collection is fast outpacing our capacity to analyze them. Astrostatistical effort has generally focused on principled analysis of individual observations, on one or a few sources at a time. But the new era of data intensive observational astronomy forces us to consider combining multiple datasets and infer parameters that are common to entire populations. Many astronomers really want to use every data point and even non-detections, but this becomes problematic for many statistical techniques.

The goal of the Workshop is to explore new problems in Astronomical data analysis that arise from data complexity. Our focus is on problems that have generally been considered intractable due to insufficient computational power or inefficient algorithms, but are now becoming tractable. Examples of such problems include: accounting for uncertainties in instrument calibration; classification, regression, and density estimations of massive data sets that may be truncated and contaminated with measurement errors and outliers; and designing statistical emulators to efficiently approximate the output from complex astrophysical computer models and simulations, thus making statistical inference on them tractable. We aim to present some issues to the statisticians and clarify difficulties with the currently used methodologies, e.g. MCMC methods. The Workshop will consist of review talks on current Statistical methods by Statisticians, descriptions of data analysis issues by astronomers, and open discussions between Astronomers and Statisticians. We hope to define a path for development of new algorithms that target specific issues, designed to help with applications to SDO, Pan-STARRS, LSST, and other survey data.

We hope you will be able to attend the workshop and present a brief talk on the scope of the data analysis problem that you confront in your project. The workshop will have presentations in the morning sessions, followed by a discussion session in the afternoons of both days.

First Announcement

Summer School in Statistics for Astronomers VI
June 7-12, 2010
with a supplement on Statistics and Computation for Astronomical Surveys
June 12-14, 2010
Registration Deadline: May 3, 2010 or when the enrollment limit reaches.
Penn State University

http://astrostatistics.psu.edu/su10/

The sixth annual Penn State Summer School in Statistics for Astronomers will be held at Penn State. The main part of the School is a 6-day course (June 7-12, 2010) in fundamental statistical inference designed to provide researchers and graduate students in the physical sciences with a strong conceptual foundation in modern statistics. We develop a repertoire of well-established techniques applicable to observational astronomy and physics. Classroom instruction is interspersed with hands-on analysis of astronomical data using the open-source R software package. The course is taught by a team of statistics and astronomy professors with opportunity for discussion of methodological issues. The program starts on Monday morning (June 7, 2010), and ends on Saturday June 12, 2010 at noon. The topics covered include:

* Exploratory data analysis
* Hypothesis testing and parameter estimation
* Regression
* Bootstrap resampling
* Model selection & goodness-of-fit
* Maximum likelihood methods & Bayes’ Theorem
* Non-parametric methods
* Monte Carlo methods
* Poisson processes
* Time series

The 2010 Summer School will be modeled on the last four Penn State Summer Schools and the two Indian Institute of Astrophysics-Penn State Summer School; see 2005, 2006, 2007, 2008 and 2009 lecture notes for the Penn State Summer Schools.

This is immediately followed by a supplementary program (June 12-14, 2010) on Statistics and Computation for Astronomical Surveys. This program starts on Saturday June 12 immediately following the main school and ends on Monday June 14 at noon. Statistical topics covered will include:

* Number count distributions (“logN-logS”) and the fundamental equation of stellar statistics
* Selection effects: truncation and censoring (Lynden-Bell, Kaplan-Meier product limit estimators)
* Classical survey biases (Eddington, Malmquist, Lutz-Kelker)
* Population modeling with hierarchical models
* Statistical cross-matching between surveys
* Introduction to Virtual Observatory software tools for querying and analyzing survey data

Participants may register for one or both programs. There is limited financial support for the program on astronomical surveys; requests for support should be sent to Tom Loredo (loredo, at astro.cornell.edu) by May 3.

Abstracts are due Sep 17.

Meeting Justification

We propose to highlight the growing use of ‘non-parametric’ techniques to distill meaningful science from today’s astronomical data. Challenges range from Kuiper objects to cosmology. We have chosen just a few ‘teaching’ examples from this lively interdisciplinary area.

Meeting Notes

This ‘Astro-Statistics’ special session is proposed in concert with an ‘Astro-Informatics’ Special Session, organized by Kirk Bourne. In this proposed ‘Non-Parametrics for the Non-Specialist’ session, we are highlighting just a few of the new, outstanding, applications. Many are coming to fruition just now, in this age of large data-sets, complex instruments, and subtleties of distilling accurate science from indirect measurements. We chose to highlight: complex models (cosmology, black hole mass distributions); and complex data, such as image (spatial); and timing analyses (e.g. transients such as the distribution of Kuiper objecs) from surveys. We invited a mixture of newer and seasoned speakers; and ones that will make good ‘teaching examples’. At the same time, we left out many new areas. Hence we are planning a lively, associated, poster session. The format will be: An Intro by one of the seasoned statisticians; followed by ‘examples’ talks by two astronomers and a physicist. Following, another of the senior statisticians will discuss the principles. Finally, a senior astrophysicist will summarize challenges for the future. We plan to leave time for one-minute poster advertisements highlighting other areas. Expected participants include: Eric Feigelson, Brandon Kelly, Meyer Pesenson, Stanislav (George) Djorgovski, Tom Loredo, Alanna Connors, Pavlos Protopapas, Katrin Heitmann, Chad Schaefer, Xiao Li Meng.

Eligibility

The candidate must be a member of SBSS (URL: www.amstat.org/membership/chapsection.pdf) or ISBA (International Society for Bayesian Analysis). Those candidates who have previously received travel support from SBSS are not eligible to participate. In addition, the candidate must be a full-time student (undergraduate, Masters, or Ph.D.) on or after September 1, 2009.

A manuscript, suitable for journal submission, is required for entry. The candidate must be the lead author on the paper, and hold the primary responsibility for the research and write-up.

The candidate must have separately submitted an abstract for JSM 2010 through the regular abstract submission process,  to present applied, computational, or theoretical Bayesian work. Papers should be submitted for presentation at the JSM as topic contributed or invited papers. Those papers not already a part of a session should be submitted online using the following settings:

(at URL: www.amstat.org/meetings/jsm/2010/index.cfm?fuseaction=abstracts):

* Abstract Type: Topic contributed
* Sub Type: Papers
* Sponsor: Section on Bayesian Statistical Science
* Organizer:  Alyson Wilson
* Organizer e-mail: agw -at- iastate.edu

Application Process

The deadline for application is Feb. 1 (same as the JSM 2010 abstract submission deadline). A formal application including the following materials should be emailed to Prof. Vanja Dukic (vanja -at- uchicago.edu):

a)      CV
b)      Abstract number (from the ASA JSM 2010 abstract submission)
c)      Letter from the major professor (advisor) or faculty co-author, verifying the student status of the candidate, and briefly describing the candidate’s role in the research and writing of the paper
d)      The manuscript, suitable for journal submission, in .pdf format.

Selection of Winners

Papers will be reviewed by a committee determined by the officers of the SBSS. Criteria for selection will include, but are not limited to, significance and potential impact of the research.  Decisions of the committee are final, and will be announced in the Spring before the JSM.

Prizes

Prizes will consist of a certificate to be presented at the SBSS section meeting and partial support (up to $1000) for attending the JSM.  Please note that the awards may be unable to cover the entirety of any winner’s travel, so winning candidates may need to supplement the SBSS award with other funds. To receive a monetary prize, the winner will need to provide proof of membership and submit travel receipts to the SBSS treasurer after the JSM.

——————————————————————
First Announcement and Call for Applications
——————————————————————

The “International Max Planck Research School for Astronomy & Cosmic Physics at the University of Heidelberg” (IMPRS-HD)

announces the

— 4th Heidelberg Summer School:

— Statistical Inferences from Astrophysical Data

— August 10-14, 2009

IMPRS Heidelberg invites graduate students and postdocs to its 4th Heidelberg Summer School. This year’s school is centered on how to draw scientific inferences from astrophysical data sets. We will also discuss proper statistical methods that are crucial for testing specific astrophysical models.

The school will present essential statistical concepts and techniques. These concepts will be illustrated through various astrophysical examples. Approaches such as Monte Carlo, maximum likelihood techniques, Bayesian statistics, parametric tests, biases in censored/incomplete data, or time-series analysis will be applied in computer exercises.

The main lecturing program is presented by invited speakers and is accompanied by practical exercises and also science talks on specific topics by local experts.

Invited lecturers are:

— David W. HOGG, New York University

— Ian McHARDY, University of Southampton

— William H. PRESS, University of Texas, Austin

Deadline for application is June 15, 2009.

Please find more information, our poster, and the application
forms under
www.mpia.de/imprs-hd/
www.mpia.de/imprs-hd/SummerSchools/2009/

A limited number of grants are available to partially cover travel expenses of participants.

IMPRS-HD is an independent part of the Heidelberg Graduate School for Fundamental Physics.

Summer School in Statistics for Astronomers V
June 1-6, 2009
Penn State University
http://astrostatistics.psu.edu/su09/

The fifth annual Penn State Summer School in Statistics for Astronomers will be held at Penn State. This is a 6-day course in fundamental statistical inference designed to provide physical scientists, particularly young researchers in astronomy, with a strong conceptual foundation in modern statistics and to develop a repertoire of well-established techniques applicable to observational astronomy. Classroom instruction is interspersed with hands-on analysis of astronomical data using the public-domain R software package. The course is taught by a team of statistics and astronomy professors with opportunity for discussion of methodological issues.

Statistical techniques covered include:

* exploratory data analysis
* hypothesis testing and parameter estimation
* regression & confidence interval estimation
* model selection & goodness-of-fit
* maximum likelihood methods & Bayes’ Theorem
* non-parametric methods
* Monte Carlo methods
* Poisson processes
* time series analysis

INTERESTED PARTICIPANTS SHOULD REGISTER AT http://astrostatistics.psu.edu/su09/reg.html
REGISTRATION DEADLINE: May 1, 2009 (or when the enrollment limit reaches)
REGISTRATION FEE: $250.

The 2009 Summer School is organized by Penn State University’s Center for Astrostatistics. It will be modeled on the last four Penn State Summer Schools and the 2008 Indian Institute of Astrophysics-Penn State Summer School. See http://astrostatistics.psu.edu/ for past lecture notes. This Web site http://astrostatistics.psu.edu/su09/ also gives information on travel, lodging, and visas.