Jun Yang, Tenure Track Assistant Professor

Jun is a statistician working on computational statistics and statistical/Bayesian learning theory. His recent interests include high-dimensional Markov chain Monte Carlo, the interplay between sampling and optimization, and statistical inference for nonlinear/non-stationary/high-dimensional time series.

Jun holds a PhD in Statistics from the University of Toronto (Canada) and has previously worked as a Florence Nightingale Bicentennial Fellow and Tutor in Computational Statistics and Machine Learning at the Department of Statistics, University of Oxford (England). He was born and raised in China and has been a Canadian citizen since December 2018.

In Copenhagen, he will work in the section for Statistics and Probability Theory. He will continue working on the development and analysis of Monte Carlo methods, including Markov chain Monte Carlo (MCMC), stochastic optimization, and variational inference, as well as their application to scientific computing, Bayesian analysis, and modern machine learning.

You can find Jun in room 04.2.17.