Dayi (David) Li (李大一)
Astrostatistics and Data Sciences
I am a Ph.D. Candidate in statistics at the University of Toronto. I am lucky to be advised by Gwendolyn Eadie, Patrick Brown, and Roberto Abraham. I am a CANSSI Ontario Multidisciplinary Doctoral (Mdoc) trainee, and my research is supported by the Data Sciences Institute Doctoral Fellowship.
Previously, I obtained my bachelor degree in financial modelling at Western University. Subsequently, I obtained my Masters degree in statistics at Western under the supervision of Ian McLeod and Pauline Barmby.
I am interested in developing spatial statistical models to solve complex astrophysical problems, particularly regarding ultra-diffuse galaxies and star clusters. I also spend my time designing and studying theoretical properties of fast, approximate Bayesian inference methods facilitated by modern machine learning methods.
A representative paper for detecting ultra-diffuse galaxies can be found at: "Poisson Cluster Process for Detecting Ultra-Diffuse Galaxies", The Annals of Applied Statistics.
A representative paper of the discovery and validation of an almost dark galaxy can be found at: "Candidate Dark Galaxy-2: Validation and Analysis of an Almost Dark Galaxy in the Perseus Cluster", The Astrophysical Journal Letters.
Media Coverage: AAS Nova; EarthSky.org
Ph.D. Candidate in Statistics, 2025 (expected), University of Toronto
M.Sc. in Statistics, 2020, Western University
H.B.Sc. in Financial Modelling, 2018, Western University (Gold Medalist)
Astrostatistics: Low-Surface Brightness Universe, Star Clusters
Spatial Models
Spatial Point Process
Hierarchical Bayesian Models
Bayesian Inference/Computation