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Budavári’s work centers on computational and statistical challenges of big data. His interests include Bayesian inference, low-dimensional embeddings, streaming algorithms, parallel processing on GPUs, scientific databases, and survey astronomy. His research has been used to understand the dynamics of vacant housing in Baltimore City, to extract high-resolution images of stars in the night sky using repeated exposures, and crossmatch astronomy catalogs.
He has made a number of contributions to computational astronomy over the years, including advances in photometric redshift estimation and the development of SkyQuery, an online query tool that combines large volumes of data from separate telescopes. He also pioneered new methodologies to streamline queries of large astronomy catalogs and simulations, such as the Sloan Digital Sky Survey, the Galaxy Evolution Explorer, Hubble Legacy Archive, and Millennium Simulation.
He has made a number of contributions to computational astronomy over the years, including advances in photometric redshift estimation and the development of SkyQuery, an online query tool that combines large volumes of data from separate telescopes. He also pioneered new methodologies to streamline queries of large astronomy catalogs and simulations, such as the Sloan Digital Sky Survey, the Galaxy Evolution Explorer, Hubble Legacy Archive, and Millennium Simulation.
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The Astronomical Journalno. 4 (2023): 174-174
Astronomical Society of the Pacific Conference Series (2019): 583-586
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Astronomical Society of the Pacific Conference Series (2017): 199-208
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