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Tamás Budavári, an associate professor in the Department of Applied Mathematics and Statistics, is known for his studies in the intersection of observational astronomy and statistics with a focus on a large-scale structure, cosmology, and galaxy evolution. He holds a joint appointment in the Department of Physics and Astronomy and a secondary appointment in the Department of Computer Science.
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.
Budavári is a member of the American Statistical Association, Society of the Hungarian Academy of Sciences, and the American Astronomical Society. He was a recipient of the Gordon and Betty Moore Fellowship, Hungarian National Graduate Fellowship, and was a Research Fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI) in 2012. His research has been funded by the National Science Foundation, Virtual Astronomical Observatory, Space Telescope Science Institute, Alfred P. Sloan Foundation, U.S. Department of Defense, Army Research Office, and the National Institutes of Health. He is an active member of JHU’s Institute for Data-Intensive Engineering and Science (IDIES) and the Steering Committee of the 21st Centuries Cities Initiative. Budavári is a founding editor of the Journal of Astronomy and Computing.
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.
Budavári is a member of the American Statistical Association, Society of the Hungarian Academy of Sciences, and the American Astronomical Society. He was a recipient of the Gordon and Betty Moore Fellowship, Hungarian National Graduate Fellowship, and was a Research Fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI) in 2012. His research has been funded by the National Science Foundation, Virtual Astronomical Observatory, Space Telescope Science Institute, Alfred P. Sloan Foundation, U.S. Department of Defense, Army Research Office, and the National Institutes of Health. He is an active member of JHU’s Institute for Data-Intensive Engineering and Science (IDIES) and the Steering Committee of the 21st Centuries Cities Initiative. Budavári is a founding editor of the Journal of Astronomy and Computing.
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arXiv (Cornell University) (2023)
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arxiv(2023)
Astrophysics Source Code Library (2021)
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American Astronomical Society Meeting Abstractsno. 1 (2021)
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Tianyi Chen,Yiwen Chen, Jingyi Gao, Peiheng Gao, Jeong Hyun Moon, Jingyi Ren, Ranran Zhu,Shanshan Song,Jeanne M. Clark,Wendy Bennett, Harold Lehman,Tamas Budavari,
biorxiv(2021)
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