I received my Ph.D. degree from the Department of Statistics at Stockholm University 2015, under the supervision of Professor Mattias Villani. I also hold a M.Sc. degree in Engineering Mathematics from Lund University (2009). I joined ACEMS in 2017, where I worked under the supervision of CI Professor Robert Kohn until 2019. I am currently a Lecturer in Statistics at the University of Technology Sydney (UTS).
My research interests lie in the area of Bayesian Statistics. In particular, I am interested in computationally challenging problems, especially in Markov chain Monte Carlo simulation algorithms and variational inference.
Monte Carlo Methods
Ph. D. in Statistics, Stockholm University, 2015
M. Sc. in Engineering Mathematics, Lund University, 2009
In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges inference methodology, in particular simulation algorithms commonly applied in Bayesian inference. These algorithms typically require repeated evaluations over the whole data set when fitting models, precluding their use in the age of so called big data.
ACEMS researchers have now provided a mathematical treatment that sheds light on the variance reduction properties of the reparameterization trick. The work was presented at the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) in Okinawa, Japan in April 2019, and was subsequently published in the conference proceedings. AISTATS is considered a top conference in Machine Learning.
Invited talks, refereed proceedings and other conference outputs
Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2018). Hamiltonian Monte Carlo with energy conserving subsampling. The 2nd International Conference on Econometrics and Statistics (EcoSta 2018).