Physicist and machine learning researcher specializing in AI-driven scientific modeling, differentiable simulation, generative models, and high-dimensional inference for complex ph
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Physicist and machine learning researcher specializing in AI-driven scientific modeling, differentiable simulation, generative models, and high-dimensional inference for complex physical systems. I bring a Ph.D.
background in theoretical and computational physics, postdoctoral research experience across astrophysics and national-lab environments, and a publication record spanning differentiable forward models, neural-physical solvers, simulation-based inference, GPU-accelerated analysis, and ML surrogates for multiscale systems. I am especially interested in building AI tools that accelerate physical simulation, inverse modeling, uncertainty quantification, and scientific discovery in industry settings.
Kavli IPMU Fellow - Kavli Institute for the Physics and Mathematics of the Universe - Kashiwanoha, JP
(2024-03)
Working on machine learning-based approaches connecting large scale structure and hydrodynamics.
Postdoctoral Research Associate - Lawrence Berkeley National Lab - Berkeley, CA
(2023-03 - 2023-11)
Focused on intersection of machine learning and astrophysical analysis.
Postdoctoral Research Associate - Princeton Astrophysical Sciences - Princeton, NJ
(2020-03 - 2022-11)
Focused on statistical analysis and machine learning of large survey data.
Graduate Student Research Associate - Lawrence Berkeley National Lab - Berkeley, CA
(2019-03 - 2019-11)
Research Associate - Princeton Plasma Physics Lab - Princeton, NJ
(2013-06 - 2013-09)
Developed algorithms in Python and C++ for calculation of Thomson scattering spectrum for ongoing fusion experiments.
Ph.D. - Physics - University of California, Berkeley (2014-09 - 2019-12)
MA - Physics - University of California, Berkeley (2014-09 - 2015-12)
B.S. - Mathematics and Physics - Yale University (2010-09 - 2014-05)
