Joseph Richards

Postdoctoral Researcher
University of California at Berkeley
Center for Time Domain Informatics
481 Evans Hall
Berkeley, CA 94720
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***NEW*** Active Learning Paper Published in ApJ!

My area of interest is Astrostatistics. Modern-day astronomical surveys produce massive data streams, requiring sophisticated statistical analysis. Currently, my main area of focus is classification of sparse and noisy astronomical time series. Upcoming photometric surveys such as LSST will observe ~105 new transient and variable sources per night. Fast and accurate classification tools are needed to determine the nature of each new source and to find the most interesting objects for follow-up observations and further astrophysical analysis. I am involved with several projects here in the Center for Time Domain Informatics to develop statistical methodology for this classification problem and implement these methods for the Palomar Transient Factory.

For my Ph.D. work, I introduced the diffusion map, a method of non-linear dimensionality reduction, to astrophysics, applying it to such diverse problems as star formation history estimation, photometric-redshift prediction, and supernova light curve typing. These methods are useful in a variety of problems, and I am currently researching the use of diffusion map to classify astronomical transients and variable stars. For a list of my research interests and projects, please visit my Research page or my CV.

Some Recent Publications

Richards, Joseph W.; Lee, Ann B.; Schafer, Chad M.; Freeman, Peter E.. (2011) Prototype Selection for Parameter Estimation in Complex Models. Annals of Applied Statistics 2012, Vol. 6, No. 1, 383-408. arXiv:1105.6344

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Long, James P.; Rice, John. (2011) Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification. The Astrophysical Journal, Volume 744, Issue 2. arXiv:1106:2832

Richards, Joseph W.; Homrighausen, Darren; Freeman, Peter E.; Schafer, Chad M.; Poznanski, Dovi. (2011) Semi-supervised Learning for Photometric Supernova Classification. Monthly Notices of the Royal Astronomical Society, Volume 419, Issue 2, pp. 1121-1135. arXiv:1103.6034

Richards, J. W.; Starr, D. L.; Butler, N. R.; Bloom, J. S.; Brewer, J. M.; Crellin-Quick, A.; Higgins, J.; Kennedy, R.; Rischard, M. (2011) On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data. ApJ, Volume 733, Issue 1.

Leal-Ferreira, M. L.; Gonçalves, D. R.; Monteiro, H.; Richards, J. W. (2011) Physico-chemical spectroscopic mapping of the planetary nebula NGC 40 and the 2D_NEB, a new 2D algorithm to study ionized nebulae. MNRAS, Volume 411, Issue 2, pp. 1395-1408.

Richards, J. W., Freeman, P. E., Lee, A. B., Schafer, C. M. (2009) Accurate parameter estimation for star formation history in galaxies using SDSS spectra. MNRAS, Vol 399, Issue 2, 1044-1057.

P. E. Freeman, J. A. Newman, A. B. Lee, J. W. Richards, C. M. Schafer. (2009) Photometric Redshift Estimation Using Spectral Connectivity Analysis. MNRAS, Volume 398, Issue 4, pp. 2012-2021.

Richards, J.W., Hardin, J. and Grosfils, E. (2010) Weighted model-based clustering for remote sensing image analysis. Computational Geosciences, Volume 14, Number 1 / January, 125-136.

Richards, J.W., P.E. Freeman, A.B. Lee, and C.M. Schafer (2009) Exploiting Low-Dimensional Structure in Astronomical Spectra. Astrophysical Journal, 691:, 32-42, 2009 January.

Updated 03/2011