Hi, I'm Xinyi Chen, a graduate student and a Future Investigator in NASA Earth and Space Science and Technology (FINESST) in the physics department at Yale University, working with Prof. Nikhil Padmanabhan on large-scale structure cosmology. My work aims to make the best use of the ongoing and upcoming large galaxy surveys to constrain cosmology and understand the nature of dark energy and inflation, currently with a two-pronged and intertwined approach using computational, statistical, machine learning, and theoretical tools. I work on the development and applications of high-fidelity initial condition reconstruction algorithms and explore optimal summary statistics that can better and more efficiently extract cosmological information from the data. In particular, I use reconstruction products and optimal statistics to constrain primordial non-Gaussianity. I am also a member of DESI and work on BAO analysis pipelines. I went to University of Michigan for undergrad, where I worked with Prof. August (Gus) Evrard on cluster cosmology and was part of the DES Collaboration. I also worked with Prof. Sally Oey on massive star formation. I grew up in the beautiful historical and cultural city of Hangzhou, China.
Besides large-scale structure cosmology, I am also working on using strong gravitational lensing to study Hubble tension with machine learning, which is another direction towards making the best use of large galaxy surveys. Additionally, I am also a Franke Interdisciplinary Graduate Research Fellow at Yale. I investigate the overlap between traditional and modern medicine with machine learning and statistical tools as a side. The study of applying machine learning in medical imaging data benefits astrophysical research for their shared data structure.
I enjoy teaching and science outreach and regularly participate in outreach events. But the greater community increasingly concerns me, so I also spend time in community service.
Outside of research, I write screenplays that explore the human condition. I also like classical music and play the piano.