Dr Jeong Eun (Kate) Lee

Senior Lecturer

Phone: +64 9 9219999 ext 5434

Email: jelee@aut.ac.nz

Postal Address:
School of Computing & Mathematical Sciences
Auckland University of Technology
Private Bag 92006, Auckland 1142, New Zealand

Links to relevant web pages:
Google Scholar


  • PhD in Statistics, Queensland University of Technology
  • MSc (1st class) in Applied Mathematics, University of Auckland
  • PGDipSci in Mathematics, University of Auckland
  • Bachelor of Technology in Optoelectronic, University of Auckland

Memberships and Affiliations:

International Society of Bayesian Analysis
New Zealand Statistical Association

Teaching Areas:

I am teaching both undergraduate and postgraduate courses.

STAT701 Statistical computing with SAS
STAT800 Stochastic modelling
STAT802 Advanced topics in analytics
COMP809 Data mining and machine learning

Sometimes, I teach undergraduate courses in mathematics.

MATH502 Algebra and discrete mathematics
MATH500 Mathematical concepts

Research Areas:

  • Bayesian analysis
  • Computational statistics and algorithms
  • Extreme value modelling
  • Big data analytics
  • Application of Bayesian inference

Research Summary:

PhD Supervision

Mohammad Sazzad Mosharrof, 2015-present, “Sources of Uncertainties in Composite Structures; Theoretical and Computational Methods” (with Dr Hyuck Chung and Prof. Jiling Cao)

Sakthithasan Sripirakas, 2012-2014, “High speed data stream mining using forest of decision tree” (with Dr Russel Pears)

MSc supervision

Wei Cui, 2014, “Human face recognition in a complex environment” (with Dr Wei Qi Yan)

Industry based project supervision

Xudong Liu (Qrious), Yibo Zhao (AirNZ)

I regularly review journal and proceeding publications.

* Computational Statistics (Springer), Australian and New Zealand Journal of Statistics, Journal of Hydrology (NZ)
* International Conference on Machine Learning (2015-now), International Conference on Artificial Intelligence and Statistics (2015-now)


Research and study leave in Semester 2, 2017.


Weekly informative reparametrisations for loatino-scale mixtures. Kamary, K., Lee, J. and Robert, C. P. Accepted JCGS.

Importance sampling schemes for evidence approximation in mixture models. Lee, J. and Robert, C. P. (2016). Bayesian Analysis. 11 (2). 573-597.

Threshold selection method using the measure of surprise. Lee, J., Fan, Y., and Sisson, S. (2015). Computational Statistics & Data Analysis. 85. 84-99.

Detecting de-lamination in composite beams using natural frequencies and the Bayesian inference. Chung, H. and Lee, J. (2015). The 22nd International Congress on Sound and Vibration. Florence, Italy.

Detecting defects in composite beams and plates using Bayesian inference. Chung, H. and Lee, J. (2014). International conference on noise and vibration engineering 2014, Leuven, Belgium.

Issues in designing hybrid algorithms. Lee, J., Robert, C. P., and Mengersen, K. L. (2013) In Case Studies in Bayesian Statistical Modelling and Analysis (eds C. L. Alston, K. L. Mengersen, A. N. Pettitt). Wiley Series in Probability and Statistics.

Population Monte Carlo algorithm in high dimensions. Lee, J., Mengersen, K. L., and McVinish, R., (2011) Methodology and Computing in Applied Probability,13, 2, 369-389.

Bayesian Inference on Mixtures of Distributions. Lee, K., Mengersen, K. L., Marin, J.-M., and Robert, C. P. (2008) In Perspectives in Mathematical Sciences. Stat. Sci. Interdiscip. Res., 7, 165-202. World Sci. Publ., Hackensack, NJ.


R-package, Ultimixt : Bayesian analysis of a non-informative parameterization for Gaussian mixture distributions. Kamary, K., and Lee, J.

Last updated: 01-Feb-2018 4.51pm

The information on this page was correct at time of publication. For a comprehensive overview of AUT qualifications, please refer to the Academic Calendar.