Somak Raychaudhury
Research Interests

Designer Algorithms for Astronomy

Large archives of astronomical data (images, spectra and catalogues) are being assembled into a database that will soon be accessible worldwide as part of the Virtual Observatory. The necessitates the development of techniques that will allow fast, automated classification and extraction of key physical properties for very large datasets, and the ability to visualise the structure of highly multi-dimensional data, and extract and study substructures in a flexible way. Since 2003, I have coordinated an effort between the School of Physics and Astronomy and the School of Computer Science at the University of Birmingham to develop a number of innovative algorithms for Astronomical data analysis and data mining. We have employed various machine learning techniques, including kernel methods, support vector machines, independent components and latent variable analysis. We have also worked on interesting astronomical applications of genetic programming and evolutionary computation and of research in computer vision.


At the the School of Physics and Astronomy, members involved in this activity are Somak Raychaudhury, Trevor Ponman, Alastair Sanderson, Ian Stevens and Bill Chaplin, and graduate students Matt Lazell and Aurelia Pascut. Past members involved in this activity include Louisa Nolan, Rowan Temple and Habib Khosroshahi. The principal collaborators from the School of Computer Science are Peter Tino, Ata Kabán, Ela Claridge and Xin Yao. This activity has yielded an E-science award from PPARC (Jianyong Sun, 2005-08), and PhD studentship from STFC (Matt Lazell, 2009-12). Also, this activity has produced the PhD theses of Juan Cuevas-Tello and Xiaoxia Wang, and parts of the theses of Nikolaos Gianniotis  and Steve Spreckley.

  1. Uncovering delayed patterns in noisy and irregularly sampled time series: an astronomy application
    Juan C. Cuevas-Tello, Peter Tino, Somak Raychaudhury, Xin Yao, Markus Harva, 2010, Pattern Recognition, 43, 1165-1179 (arXiv/0908.3706)
  2. Bayesian estimation of time delays between unevenly sampled signals
    Markus O. Harva and Somak Raychaudhury, 2008, Neurocomputing, 72, 32-38
  3. Young stellar populations in early-type galaxies in the Sloan Digital Sky Survey
    Nolan Louisa A., Raychaudhury Somak and Kaban, Ata, 2007, MNRAS, 375, 381-387 (astro-ph/0608623) 
  4. How accurate are the time delay estimates in gravitational lensing?
    Juan C. Cuevas-Tello, Peter Tino & Raychaudhury Somak, 2006, Astron. Astrophys., 454, 695-706 (astro-ph/0605042)
  5. Finding young stellar populations in early-type galaxies from an independent component analysis of their UV-optical spectra
    Nolan Louisa A., Harva Markus O.,  Kaban Ata and Raychaudhury Somak, 2005, MNRAS, 366, 321-338 (astro-ph/0511503)

    Refereed Proceedings
  6. Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model
    Nikolaos Gianniotis, Peter Tino, Steven Spreckley, Somak Raychaudhury, 2009, In Visualization of Structured Data via Generative Probabilistic Modeling In Artificial Neural Networks (ICANN 2009), pp. 567-576, Lecture Notes in Computer Science, vol. 5768, Springer-Verlag, 2009. ICANN 2009. September 14-17, Limassol, Cyprus Proceedings Link (arXiv/0909.3882)
  7. Fast Parzen Window Density Estimator
    Xiaoxia Wang, Peter Tino, Mark A. Fardal, Somak Raychaudhury and Arif Babul In Proceedings of the 2009 International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, GA, June 2009. Proceedings Link (ADS Link)
  8. Robust visual mining of data with error information
    Jianyong Sun, Ata Kaban and Somak Raychaudhury. In Proceedings of the 11-th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD07). 17-21 September 2007, Warsaw, Poland; pp.573-580. Proceedings Link
  9. Robust mixtures in the presence of measurement errors
    Jianyong Sun, Ata Kaban and Somak Raychaudhury. In Proceedings of the 24-th Annual International Conference on Machine Learning 2007 (ICML07) , (Ed.) Z. Ghahramani. June 20-24, Oregon State University, Corvallis, OR, USA, pp. 847-854. (arXiv/0709.0928)
  10. On class visualisation for high dimensional data: Exploring scientific data sets
    Ata Kaban, Jianyong Sun, Somak Raychaudhury and Louisa Nolan, 2006, Lecture Notes in Computer Science, Vol. 4265. Discovery Science 2006. ISBN 978-3-540-45375-8. Springer: Berlin /Heidelberg, 2006, p. 125-136 (astro-ph/0609094)
  11. A kernel-based approach to estimating phase shifts between irregularly sampled time series: an application to gravitational lenses
    Juan C. Cuevas-Tello, Peter Tino and Somak Raychaudhury, 2006, Lecture Notes in Computer Science, Vol. 4212. Machine Learning: ECML 2006. ISBN 978-3-540-45375-8. Springer: Berlin / Heidelberg, 2006, p. 614-621.
  12. Bayesian Estimation of Time Delays Between Unevenly Sampled Signals
    Markus Harva and Somak Raychaudhury, 2006, Proceedings of IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING, (MLSP'06) September 2006, Maynooth, Ireland, pp. 111-116.
  13. Finding Young Stellar Populations in Elliptical Galaxies from Independent Components of Optical Spectra
    Ata Kaban, Louisa Nolan, Somak Raychaudhury, 2005, SIAM International Conference on Data Mining (SDM05) 21--24 April 2005, Newport Beach California, USA. (astro-ph/0505059)
  14. Mapping the physical properties of cosmic hot gas with hyper-spectral imaging
    Ela Claridge, Mark O'Dwyer, Trevor Ponman, and Somak Raychaudhury, 2005, in IEEE Workshop on Applications of Computer Vision (WACV2005), January 2005, pp 185-190. (astro-ph/0505165)
  15. An Evolutionary Approach to Modelling Radial Brightness Distributions in Elliptical Galaxies
    J. Li, X. Yao, C. Frayn, H. G. Khosroshahi and S. Raychaudhury, 2004, Lecture Notes in Computer Science, Vol. 3242. Parallel Problem Solving from Nature - PPSN VIII. ISBN 978-3-540-23092-2. Springer: Berlin / Heidelberg, 2004, p. 591-601

Full List of publications