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Shirley Ho's research interests have ranged from using machine learning and statistics to tackle fundamental challenges in cosmology to finding new structures in our own Milky Way. Ho has broad expertise in theoretical astrophysics, observational astronomy and data science. Ho’s recent interest has been on understanding and developing novel tools in machine learning techniques, and applying them to astrophysical challenges. Her goal is to understand the universe’s beginning, evolution and its ultimate fate.
Ho works with a wonderful group of international collaborators both within the Cosmology x Data Science Group at Flatiron Institute, at Department of Astrophysical Sciences at Princeton University, and beyond.
Ho works with a wonderful group of international collaborators both within the Cosmology x Data Science Group at Flatiron Institute, at Department of Astrophysical Sciences at Princeton University, and beyond.
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论文共 293 篇作者统计合作学者相似作者
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ASTROPHYSICAL JOURNALno. 1 (2024): 19
Joshua Fagin, Ji Won Park, Henry Best, James H. H. Chan,K. E. Saavik Ford,Matthew J. Graham,V. Ashley Villar,Shirley Ho, Matthew O’Dowd
Monthly Notices of the Royal Astronomical Society: Lettersno. 1 (2023): L173-L178
Michael McCabe,Bruno Régaldo-Saint Blancard, Liam Holden Parker,Ruben Ohana,Miles Cranmer,Alberto Bietti,Michael Eickenberg,Siavash Golkar, Geraud Krawezik,Francois Lanusse,Mariel Pettee,Tiberiu Tesileanu,
CoRR (2023)
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François Lanusse, Liam Holden Parker,Siavash Golkar,Miles D. Cranmer,Alberto Bietti,Michael Eickenberg, Géraud Krawezik, Michael McCabe,Ruben Ohana,Mariel Pettee,Bruno Régaldo-Saint Blancard,Tiberiu Tesileanu,
CoRR (2023)
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