Dayi (David) Li (李大一)

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.

Education:

• 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)

Research Interests:

• Astrostatistics: Low-Surface Brightness Universe, Star Clusters

• Spatial Models

• Spatial Point Process

• Hierarchical Bayesian Models

• Bayesian Inference/Computation