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 can be found at: "Poisson Cluster Process for Detecting Ultra-Diffuse Galaxies", The Annals of Applied Statistics.
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