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Peter Melchior is an Assistant Professor in the Department of Astrophysical Sciences at Princeton University, specializing in Statistical Astronomy. His research focuses on creating novel techniques that enhance the extraction of information from messy astronomical observations, which often include noisy, incomplete, or contaminated data. He leads several initiatives, including the Princeton Astro Data Lab and the Dynamical Learning Lab, where he develops algorithms that leverage deep learning and physical principles to solve complex problems in astronomy. Melchior's work encompasses signal separation, data fusion, and rapid inference, contributing significantly to large astronomical surveys. He is a Principal Investigator on multiple NSF and NASA-funded projects, aiming to advance analytical methods for both ground-based and space-based imaging, including efforts to discover Earth-like exoplanets. His research extends into the realm of physics-based machine learning, particularly in hydrology, where he leads the HydroGEN project, which aims to predict drought conditions and optimize water resource management throughout the continental United States. He actively engages with undergraduate and graduate students, providing opportunities for research projects at Princeton.
Princeton University • Princeton, NJ
Focuses on Statistical Astronomy and leads several research labs.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.