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Peter Melchior's research interests lie in statistical methods for large surveys. He leads the Astronomy Data Group, where he develops algorithms that accurately capture the properties of billions of stars and galaxies, addressing limitations posed by real-world instruments. He holds a joint position within the Department of Astrophysical Sciences. His central research question revolves around how to optimally combine multiple data sets to extract information that surpasses individual analyses. His group designs systems that integrate data from upcoming surveys such as the Large Synoptic Survey Telescope (LSST), Euclid, and the Wide-Field Infrared Survey Telescope (WFIRST) at the pixel level. He develops techniques for source separation, mixture modeling, and data fusion, utilizing increasingly advanced methods including neural networks. As a Principal Investigator, he oversees a project funded by the Schmidt Futures Foundation that employs modern statistical tools to optimize target selection for the upcoming PFS survey. Additionally, he organizes the Undergraduate Summer Research Program 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.