معرفی
Xinyi Chen is a Postdoctoral Fellow at the Perimeter Institute for Theoretical Physics. Her research focuses on large-scale structure cosmology, dark energy, and primordial non-Gaussianity, leveraging machine learning and computational tools to analyze galaxy surveys like DESI. She holds a Ph.D. from Yale University (2024), where she developed novel methods for initial condition reconstruction and optimal statistical analysis. Her work bridges astrophysics and interdisciplinary applications, including medical imaging and gravitational lensing studies of Hubble tension.
- Education: Ph.D. in Physics (2024), Yale University; B.S. in Physics, University of Michigan.
- Research Highlights: Hybrid reconstruction algorithms, BAO analysis pipelines, DESI collaboration, and alternative PNG constraint methods.
- Awards: 2021 NASA FINSST Award.
She explores applications of machine learning in both astrophysics and medicine, as a Franke Interdisciplinary Fellow. Active in teaching and outreach, she also contributes to Yale's cosmology seminar organization and engages in creative writing and classical music.
