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/

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.

A postdoctoral position is available at the University of California, Berkeley for an individual who can lead an effort in real-time classification of astronomical time-series data for the purpose of extraction of novel science. The project is sponsored by a new Cyber-enabled Discovery and Innovation (CDI) grant from the National Science Foundation (NSF; http://128.150.4.107/awardsearch/showAward.do?AwardNumber=0941742 ).

The main goal of this project it to produce a framework (including new theoretical/algorithmic constructs) for extracting novel science from large amounts of data in an environment where the computational needs vastly outweigh the available facilities, and intelligent (as well as dynamic) resource allocation is required. This work will draw from current research in statistics, database engineering, computational science, time-domain astronomy, and machine learning and is expected to lead to applications beyond astronomy. The collaboration has access to proprietary astronomical datasets. We hope to build a system eventually capable of ingesting, assimilating, and creating “new knowledge” from massive data streams expected from new projects, such as the Large Synoptic Survey Telescope. The collaboration also has access to large-scale computing facilities through the Center for Information Technology Research in the Interest of Society (CITRIS) at Berkeley, at Lawrence Berkeley National Laboratory (LBNL), and and through cloud computing time donated by industry partners.

This work will be directed by Prof. Joshua Bloom in the Astronomy Department but the position calls for strong interactions with other senior members of the collaboration in other departments (Martin Wainwright, EECS and Statistics; Nourredine El Kouroui, Statistics; John Rice, Statistics; Massoud Nikravesh, CITRIS; Peter Nugent, LBNL; Horst Simon, LBNL). Experience and a demonstrated interest working with graduate students across these disciplines is also encouraged.

Minimum qualifications include a Ph.D. in Computer Science, Electrical Engineering, Statistics, Astronomy or closely related field is required. The strongest candidates will have demonstrated success in conducting original research in statistics and/or machine learning and should have a deep understanding and/or interest in topics of time-domain Astronomy. Work will commence no later than 1 August 2010. The appointment may start on an earlier date, if mutually convenient (funding is already available to start as early as Spring 2010). The initial appointment is for two years, with renewal expected if progress is satisfactory and funds continue to be available. The starting salary will be commensurate with experience, and competitive with other postdoctoral positions. Please e-mail a short research statement, resume, list of publications, and copies of two recent publications (preprints or reprints) so that they arrive by the 1 February 2010 deadline to Prof. Joshua Bloom, at the above address. To receive full consideration, applicants should arrange to have letters of references from three individuals sent to Prof. Bloom by the 1 February 2010 due date (letters may also be emailed directly by the referees). Immigration status of non-citizens should be stated in the resume.

——————————————————————
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.