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.

[arxiv:0906.3662] The Statistical Analysis of fMRI Data by Martin A. Lindquist
Statistical Science, Vol. 23(4), pp. 439-464

This review paper offers some information and guidance of statistical image analysis for fMRI data that can be expanded to astronomical image data. I think that fMRI data contain similar challenges of astronomical images. As Lindquist said, collaboration helps to find shortcuts. I hope that introducing this paper helps further networking and collaboration between statisticians and astronomers.

List of similarities

• data acquisition: data read in frequency domain and image reconstruction via inverse Fourier transform. (To my naive eyes, It looks similar to Power Spectrum Analysis for cosmic microwave background (CMB) data).
• amplitudes or coefficients are cared and analyzed, not phase nor wavelets.
• understanding data:brain physiology or physics like cosmological models that describe data generating mechanism.
• limits in/trade-off between spatial and temporal resolution.
• understanding/modeling noise and signal.

These similarities seem common for statistically analyzing images from fMRI or telescopes. Notwithstanding, no astronomers can (or want) to carry out experimental design. This can be a huge difference between medical and astronomical image analysis. My emphasis is that because of these commonalities, strategies in preprocessing and data analysis for fMRI data can be shared for astronomical observations and vise versa. Some sloggers would like to check Section 6 that covers various statistical models and methods for spatial and temporal data.

I’d rather simply end this posting with the following quotes, saying that statisticians play a critical role in scientific image analysis.

There are several common objectives in the analysis of fMRI data. These include localizing regions of the brain activated by a task, determining distributed networks that correspond to brain function and making predictions about psychological or disease states. Each of these objectives can be approached through the application of suitable statistical methods, and statisticians play an important role in the interdisciplinary teams that have been assembled to tackle these problems. This role can range from determining the appropriate statistical method to apply to a data set, to the development of unique statistical methods geared specifically toward the analysis of fMRI data. With the advent of more sophisticated experimental designs and imaging techniques, the role of statisticians promises to only increase in the future.

A full spatiotemporal model of the data is generally not considered feasible and a number of short cuts are taken throughout the course of the analysis. Statisticians play an important role in determining which short cuts are appropriate in the various stages of the analysis, and determining their effects on the validity and power of the statistical analysis.

• [astro-ph:0805.3532] Balan and Lahav
  ExoFit: Orbital Parameters of Extra-solar Planets from Radial Velocities (MCMC)

• [astro-ph:0805.3983] R. G. Carlberg et al.
  Clustering of supernova Ia host galaxies (Jackknife method is utilized).

• [astro-ph:0805.4005] Kurek, Hrycyna, & Szydlowski
  From model dynamics to oscillating dark energy parametrisation (Bayes factor)

• [astro-ph:0805.4136] C. Genovese et al.
  Inference for the Dark Energy Equation of State Using Type Ia Supernova Data

• [math.ST:0805.4141] C. Genovese et al.
  On the path density of a gradient field (detecting filaments via kernel density estimation, KDE)

• [astro-ph:0805.4342] C. Espaillat et al.
  Wavelet Analysis of AGN X-Ray Time Series: A QPO in 3C 273?

• [astro-ph:0805.4414] Tegmark and Zaldarriaga
  The Fast Fourier Transform Telescope

• [astro-ph:0805.4417] A. Georgakakis et al.
  A new method for determining the sensitivity of X-ray imaging observations and the X-ray number counts

• [stat.AP:0805.4598] E. Anderes et al.
  Maximum Likelihood Estimation of Cloud Height from Multi-Angle Satellite Imagery

In addition to Bayesian methodologies, like this week’s astro-ph, studies on characterizing empirical spatial distributions of voids and galaxies frequently appear, which I believe can be enriched further with the ideas from stochastic geometry and spatial statistics. Click for what was appeared in arXiv this week.

• [astro-ph:0805.0156]R. D’Abrusco, G. Longo, N. A. Walton
  Quasar candidates selection in the Virtual Observatory era

• [astro-ph:0805.0201] S. Vegetti& L.V.E. Koopmans
  Bayesian Strong Gravitational-Lens Modelling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies (many like to see this paper: nest sampling implemented, discusses penalty function and tessllation)

• [astro-ph:0805.0238] J. A. Carter et al.
  Analytic Approximations for Transit Light Curve Observables, Uncertainties, and Covariances

• [astro-ph:0805.0269] S.M.Leach et al.
  Component separation methods for the Planck mission

• [astro-ph:0805.0276] M. Grossi et al.
  The mass density field in simulated non-Gaussian scenarios

• [astro-ph:0805.0790] Ceccarelli, Padilla, & Lambas
  Large-scale modulation of star formation in void walls
  [astro-ph:0805.0797] Ceccarelli et al.
  Voids in the 2dFGRS and LCDM simulations: spatial and dynamical properties

• [astro-ph:0805.0875] S. Basilakos and L. Perivolaropoulos
  Testing GRBs as Standard Candles

• [astro-ph:0805.0968] A. A. Stanislavsky et al.
  Statistical Modeling of Solar Flare Activity from Empirical Time Series of Soft X-ray Solar Emission

• [astro-ph:0804.2904]M. Cruz et al.
  The CMB cold spot: texture, cluster or void?

• [astro-ph:0804.2917] Z. Zhu, M. Sereno
  Testing the DGP model with gravitational lensing statistics

• [astro-ph:0804.3390] Valkenburg, Krauss, & Hamann
  Effects of Prior Assumptions on Bayesian Estimates of Inflation Parameters, and the expected Gravitational Waves Signal from Inflation

• [astro-ph:0804.3413] N.Ball et al.
  Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX (Another related publication [astro-ph:0804.3417])

• [astro-ph:0804.3471] M. Cirasuolo et al.
  A new measurement of the evolving near-infrared galaxy luminosity function out to z~4: a continuing challenge to theoretical models of galaxy formation

• [astro-ph:0804.3475] A.D. Mackey et al.
  Multiple stellar populations in three rich Large Magellanic Cloud star clusters

• [stat.ME:0804.3853] C. R\”over , R. Meyer, N. Christensen
  Modelling coloured noise (MCMC & sunspot data)

Subject : Special Joint CS and Physics Colloquium
Title : How Astronomers, Computer Scientists and Statisticians are working together to tackle hard problems in astronomy
Speaker: Pavlos Protopapas
Date : Thursday February 7
Time : 3:15 pm
Place : Nelson Auditorium, Anderson Hall (Click for the map, 200 College Ave, Medford, MA, I think)
Abstract:
New astronomical surveys such as Pan-STARRS and LSST are under development and will collect petabytes of data. These surveys will image large areas of sky repeatedly to great depth, and will detect vast numbers of moving, variably bright, and transient objects. The data product of these surveys is series of observations taken over time, or light-curves.

The IIC has established an inter-disciplinary Center for Time Series with an immediate focus on astronomy. I will present three research topics currently being pursued at the IIC that require expertise from astronomy, computer science and statistics. These are: identifying novel astronomical phenomena in large light-curve datasets, searching for rare phenomena such as extra-solar planets, and efficiently searching for significant events such as occultations of stars by small objects in the outer reaches of our solar system.

Pavlos Protopapas is a senior scientist at the IIC and Harvard-Smithsonian Center for Astrophysics. His research interests spans the outer solar system, extra-solar planets and gravitational lensing. He specializes in analyzing large collections of astronomical data, with a toolbox drawn from data-mining, computer science and statistics.