Dayi (David) Li

Ph.D. Student at the University of Toronto

CANSSI Ontario Multidisciplinary Doctoral (Mdoc) Trainee


I am a Ph.D. student in statistics advised by Gwen Eadie, Patrick Brown, and Roberto Abraham at the University of Toronto. I am a CANSSI Ontario Multidisciplinary Doctoral (Mdoc) trainee in statistics and astronomy. My research interests lie in the emerging new field of astrostatistics as well as spatial statistics and Bayesian computation. My Ph.D. work will be mainly focusing on developing novel methodology in spatial statistcs to address problems regarding the distributional properties of various astronomical objects. Currently, the objects under consideration are ultra-diffuse galaxies.

Before coming to UofT, I obtained my bachelor degree in financial modelling in 2018 at Western University. Subsequently, I obtained my Masters degree in statistics in 2020 at Western University under the supervision of Ian McLeod and Pauline Barmby where I worked on spatial point process modelling of stellar objects in the M33 galaxy. I also worked a little bit on faciliating Hamiltonian Monte Carlo for certain models with intractable likelihood functions.

In general, I believe there is still an inadequacy of rigorous applications of advanced statistical methods in astronomy. Filling this inadequacy can bring about new understanding and discovery in astronomy and help us better understand the origin of the Universe. Therefore, I am interested in pushing proper statistical application in astronomy as well as developing new statistical methods to tackle challenging astronomical questions.


  • Astrostatisics
  • Spatial Statistics
  • Bayesian Inference and Computation
  • Statistical Pattern Recognition


  • Ph.D., Statistics, 2024 (expected)

    University of Toronto

  • M.Sc., Statistics, 2020

    Western University

  • Honors B.Sc., Financial Modelling, 2018

    Western University


Coming soon…

Recent & Upcoming Talks

Gibbs Point Process Model for Objects in the Star Formation Complexes of M33


Gibbs Point Process Model for Young Star Clusters in M33

We demonstrate the power of Gibbs point process models from the spatial statistics literature when applied to studies of resolved …