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Peter Melchior's research interests lie in statistical methods for large surveys in astronomy. He leads the Astronomy Data Group, developing algorithms that accurately capture properties of billions of stars and galaxies, considering the limitations of real-world instruments. Melchior holds a joint position in the Department of Astrophysical Sciences and the Center for Statistics and Machine Learning. His central research question involves optimally combining multiple data sets to extract more information than individual analyses provide. His group is designing systems to combine data from upcoming surveys such as LSST, Euclid, and WFIRST at the pixel level. He develops techniques for source separation, mixture modeling, and data fusion, increasingly utilizing proximal techniques and neural networks. Melchior is the principal investigator on a project funded by the Schmidt Futures Foundation, aimed at optimizing target selection for the upcoming PFS survey using modern statistical tools. Moreover, he organizes the Undergraduate Summer Research Program and the Data Science Seminar.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.