Current Research Interest

My main research interest has been large-scale inference and massive-data analysis, where the data are usually very high-dimensional and one must estimate very large numbers of parameters or test very large numbers of hypotheses simultaneously. The setting is frequently found in many scientific areas, e.g. genomics, astronomy, functional Magnetic Resonance Imaging (fMRI), and image processing. Advances in large-scale inferences enable faster exactration of useful information in various scientific fieds and broaden the scope of theory and methodology in statistics.

Theory

Larg-Scale Multiple Hypotheses Testing: inference on the proportion of non-null effects, inference on null parameters, False Discovery Rate (FDR)-controlling methodology.
High Dimensional inference: classification, simultaneous estimation and variable selection by exploiting sparsity, basis pursuit through L^1 penalization, uncertainty principle.
Others: Decision Theory and Asymptotics, Random Matrix Theory (RMT), Wavelets and Signal Processing.

Applications

Astronomy and Cosmology: non-Gaussian signature detection in Cosmic Micorwave Background (CMB).
Genomics: gene microarray, Comparative Genomic Hybridization (CGH), protein mass spectroscopy.
Others: computer security, watermarking.
Link to Astrostatistics group at Purdue
Link to Internation Group of Computational Astrostatistics Group

Reports

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Click here to see reports listed by areas