Jun Yang

ABOUT ME

I am a Ph.D Candidate in Statistics at University of Toronto, advised by Daniel M. Roy and Jeffrey S. Rosenthal. I also work closely with Zhou Zhou.

Before coming to Toronto, I received a M.Sc. in Applied Mathematics from Queen's University and B.Eng. in Electronic Engineering from Tsinghua University.

I am affiliated with Vector Institute. I spent Spring 2017 at UC Berkeley, as a visiting student researcher at Simons Institute.

RESEARCH

My current research interests include statistical learning theory, computational statistics, high-dimensional Bayesian inference, and applied probability.

Recent work:
• On Fast Rate PAC-Bayes Bounds
  Jun Yang*, Shengyang Sun*, Daniel M. Roy, and Roger Grosse
  in preparation.
  
• A Bayesian Decision-Theoretic Analysis of Bayesian Inference under Model Misspecification
  Jun Yang, Yanbo Tang, Zacharie Naulet, and Daniel M. Roy
  in preparation.
  | early slides
  
• State-Domain Change Point Detection for Nonlinear Time Series
  (with Yan Cui and Zhou Zhou)
  in preparation.
  
• Optimal Scaling of Metropolis Algorithms on General Target Distributions
  Jun Yang, Gareth O. Roberts, and Jeffrey S. Rosenthal
  in preparation.
  
• Spectral Inference under Complex Temporal Dynamics
  Jun Yang and Zhou Zhou
  | arXiv:1812.07706
• Complexity Results for MCMC derived from Quantitative Bounds
  Jun Yang and Jeffrey S. Rosenthal
  | arXiv:1708.00829
• On Bounding the Union Probability using Partial Weighted Information
  Jun Yang, Fady Alajaji, and Glen Takahara
  Statistics and Probability Letters, 116, 38–44, 2016.
  | arXiv:1506.08331
• Lower Bounds on the Probability of a Finite Union of Events
  Jun Yang, Fady Alajaji, and Glen Takahara
  SIAM Journal on Discrete Mathematics, 30(3), 1437–1452, 2016.
  | arXiv:1401.5543

OTHERS

Activities, teaching, non-work page (under construction).