ABOUT ME
I am a Ph.D Candidate in Statistics at University of Toronto, advised by Daniel M. Roy and Jeffrey S. Rosenthal. I am also affiliated with the Vector Institute.
I spent Spring 2017 at Simons Institute, UC Berkeley as a visiting student researcher.
Before coming to Toronto, I received a M.Sc. in Applied Maths from Queen's University
and B.Eng. in Electronic Engineering from Tsinghua University.
RESEARCH
My current research interests include statistical learning theory, computational statistics, high-dimensional Bayesian inference, and applied probability.
Recent work:
- A Bayesian Decision-Theoretic Analysis of Bayesian Inference under Model Misspecification
Jun Yang, Yanbo Tang, Zacharie Naulet, and Daniel M. Roy
- Optimal Scaling of Metropolis Algorithms on General Target Distributions
Jun Yang, Gareth O. Roberts, and Jeffrey S. Rosenthal
- Spectral Inference under Complex Temporal Dynamics
Jun Yang and Zhou Zhou
- 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.
| pdf
Link to the full list of preprints and publications.
OTHERS
Activities, teaching, non-work page (under construction).
