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Research interests
Dr. Scalzo’s research focuses on applications of Bayesian reasoning in the natural sciences. Inverse problems -- inferring the underlying or true state of some system using sparse and noisy data from (multiple) real-world sensors -- forms a common theme in his work. Examples in astrophysics include measuring the energy spectra of astrophysical gamma-ray sources, inferring the kind of star that produced a supernova based on how quickly it fades after explosion, and removing instrumental artifacts from three-dimensional (2 spatial + 1 spectroscopic) "data cubes" of galaxies.
His more recent work with CTDS spans a range of topics, including the time-dependent drivers of the insulin response in mouse cells, the construction of three-dimensional models of geological formations beneath the Earth's surface, and experimental design for air quality monitoring and battery performance optimization. His methods-oriented research includes topics in Bayesian statistics such as prior elicitation, hierarchical or graphical Bayesian models, and advanced Markov chain Monte Carlo sampling techniques.
Dr. Scalzo’s research focuses on applications of Bayesian reasoning in the natural sciences. Inverse problems -- inferring the underlying or true state of some system using sparse and noisy data from (multiple) real-world sensors -- forms a common theme in his work. Examples in astrophysics include measuring the energy spectra of astrophysical gamma-ray sources, inferring the kind of star that produced a supernova based on how quickly it fades after explosion, and removing instrumental artifacts from three-dimensional (2 spatial + 1 spectroscopic) "data cubes" of galaxies.
His more recent work with CTDS spans a range of topics, including the time-dependent drivers of the insulin response in mouse cells, the construction of three-dimensional models of geological formations beneath the Earth's surface, and experimental design for air quality monitoring and battery performance optimization. His methods-oriented research includes topics in Bayesian statistics such as prior elicitation, hierarchical or graphical Bayesian models, and advanced Markov chain Monte Carlo sampling techniques.
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PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFICno. 1053 (2023): 114501-114501
INTERNATIONAL JOURNAL OF REMOTE SENSINGno. 5 (2020)
Lake Ee Quek,James R. Krycer,Satoshi Ohno,Katsuyuki Yugi,Daniel J. Fazakerley,Richard Scalzo, Sarah D. Elkington,Ziwei Dai,Akiyoshi Hirayama,Satsuki Ikeda,Futaba Shoji,Kumi Suzuki,
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