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I am Senior Lecturer at the Business Analytics discipline, the University of Sydney Business School, and an Associate Investigator in the Australian Research Council's Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

I received a PhD in Statistics from the National University of Singapore, a Master and a Bachelor in Mathematics from the Vietnam National University, Hanoi. Before joining the University of Sydney, I worked as a postdoctoral research fellow at the University of New South Wales.

Precisely speaking, I work part-time as a researcher and full-time as a babysitter. Here are my two most important papers EVER!

Address: Rm 4091, Business School Building (H70), University of Sydney, NSW 2006, Australia

T +61 2 8627 4752 | F +61 2 9351 6409 | E minh-ngoc dot tran at sydney dot edu dot au

News

  • Aug 2019: My new paper Variational Bayes on Manifolds (with D Nguyen and D Nguyen) develops an efficient manifold VB method that exploits both the geometry structure of the constrained variational parameter space, and the information geometry of the approximating family. Manifold VB is more stable and less sensitive to initialization. The paper also establishes a sharp convergence rate.
  • June 2019: Our paper Hamiltonian Monte Carlo with energy conserving subsampling (with Dang, Quiroz, Kohn and Villani) has been accepted by Journal of Machine Learning Research. The paper develops an HMC methodology that works with large data.
  • June 2019: A full-text version of our paper "A long short-term memory stochastic volatility model" (with Nguyen, Gunawan and Kohn) is available here. The paper combines the state-of-the-art LSTM technique in Deep Learning with Stochastic Volatility modelling in financial econometrics, and introduces the so-called LSTM-SV model for volatility modelling. It's shown in a range of examples that the LSTM-SV model is able to capture interesting underlying patterns and has an impressive predictive performance.
  • June 2018: Our first paper (with D Gunawan, S Brown, R Kohn) on experimental psychology is available here. More to come.
  • June 2018: Our first paper Bayesian Deep Net GLM and GLMM of the Bayesian deep learning project is available. More to come. Software packages are available here. Accepted by JCGS in Jan 2019.
  • May 2018: Our new paper (with D Gunawan, R Kohn, M Quiroz, K Dang) describes how to do Bayesian inference for complex models with big data, by a non-trivial combination of sophisticated techniques MCMC, annealing SMC, HMC and subsampling.
  • May 2018: I was invited to give a talk at the conference Bayesian Statistics in the Big Data Era organised by Kerrie Mengersen, Pierre Pudlo and Christian Roberts, 26-30/11 Marseille, France.
  • May 2018: Our new paper (with D Gunawan, C Carter and R Kohn) "Flexible Density Tempering Approaches for State Space Models with an Application to Factor Stochastic Volatility Models" is arXived
  • Mar 2018: A new version of our paper "The block-Poisson estimator for exact subsampling MCMC" is available on ArXiv. The paper shows that it is possible to obtain exact Bayesian inference with MCMC in big data, even only data subsets are used within MCMC iterations.
  • Jan 2018: Our paper (with Matias Quiroz, Mattias Villani, Robert Kohn) "Speeding Up MCMC by Efficient Data Subsampling" has been accepted by Journal of the American Statistical Association.
  • I will be giving a short course on "Bayesian Computation for Big Models Big Data" at IRTG Summer Camp, Humboldt-Universit├Ąt zu Berlin, 11-14 July 2018.
  • I will be on sabbatical leave from June 2018, and will be visiting and giving talks at several universities in Europe, Hong Kong and Singapore.
  • Fantastic news! our research is funded by an ARC DP grant 2018-2020, $348,912. This is a joint project with Robert Kohn (UNSW) and Scott Brown (UoN).
  • I gave a plenary invited talk on Deep Learning at the 2nd Vietnam International Conference on Applied Mathematics, HCM city, 15-18 Dec, 2017.
  • I gave an invited talk on "Bayesian Computation for Big Models Big Data" at the Statistical Challenges in Astronomy workshop, UNSW, 7-8 Dec 2017.