Dr Kate Jeong Eun Lee

BTech (UoA), MSc (UoA), PhD (QUT)

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Research | Current

My primary research goals are directed toward understanding statistical inference and related computations in the Bayesian framework. 

My research interests include;

  • Bayesian statistics
  • Monte Carlo methods
  • Mixtures
  • Inference using approximations due to expensive posterior distributions
  • Inverse problem
  • Application of Bayesian inference - I have worked on problems in food science, sport science and image detection.

Please see Google scholar for my publications.


Teaching | Current

2021 STATS 762 (S1) STATS 331 and 201/208 (S2)

2020 STATS 762 and STATS 201/208 (S1) and STATS 331 (S2)

2019 STATS 762 (S1) and STATS 201/208 and STATS 331 (S2)

Postgraduate supervision


2021-now, Zhengjie (Jeff) Shi, "Efficient algorithms for federated learning"

2019-now, Innocenter Moraa Amima, "Integrative analysis of high-dimensional data with applications to Wine Science"

2019-now, Yifu Tang, "Bayesian nonparametric analysis of non-Gaussian time series"

2019-now, Yixuan Liu, "Robust Bayesian Analysis of Multivariate Time Series"

2015-2017, Mohammad Sazzad Mosharrof, “Sources of Uncertainties in Composite Structures; Theoretical and Computational Methods”

2012-2014, Sakthithasan Sripirakas, “High speed data stream mining using forest of decision tree”


Undergraduate Adviser

Areas of expertise

Bayesian analysis, Monte Carlo method, Mixtures, Extremes

Selected publications and creative works (Research Outputs)

As of 29 October 2020 there will be no automatic updating of 'selected publications and creative works' from Research Outputs. Please continue to keep your Research Outputs profile up to date.
  • Cahill, M. J., Oliver, J. L., Cronin, J. B., Clark, K., Cross, M. R., Lloyd, R. S., & Lee, J. E. (2020). Influence of Resisted Sled-Pull Training on the Sprint Force-Velocity Profile of Male High-School Athletes. Journal of Strength and Conditioning Research, 34 (10), 2751-2759. 10.1519/JSC.0000000000003770
  • Lee, J. E., Nicholls, G. K., & Xing, H. (2020). Distortion estimates for approximate Bayesian inference. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), 124. Virtual: Association for Uncertainty in Artificial Intelligence. Related URL.
  • Lee, J., Uthoff, A., Oliver, J., Cronin, J., Winwood, P., & Harrison, C. (2019). Resisted Sprint Training in Youth; The Effectiveness of Backward vs. Forward Sled Towing on Speed, Jumping, and Leg Compliance Measures in High-School Athletes. The Journal of Strength & Conditioning Research10.1519/JSC.0000000000003093
    URL: http://hdl.handle.net/2292/47224
  • Rousseau, J., Grazian, C., & Lee, J. E. (2019). Bayesian mixture models: Theory and methods. In S. Fruhwirth-Schnatter, G. Celeux, C. P. Robert (Eds.) Handbook of mixture analysis (pp. 55-75). Boca Raton, Florida, USA: Chapman and Hall/CRC. Related URL.
    URL: http://hdl.handle.net/2292/45273
  • Lee, J. E., Nicholls, G. K., & Ryder, R. J. (2019). Calibration Procedures for Approximate Bayesian Credible Sets. Bayesian Analysis, 14 (4), 1245-1269. Related URL.
  • Villa, C., & Lee, J. E. (2019). A Loss-Based Prior for Variable Selection in Linear Regression Methods. Bayesian Analysis10.1214/19-BA1162
  • Lee, J. E., Nicholls, G., & Xing, H. (2019). Calibrated Approximate Bayesian Inference. Proceedings of ICML, 97, 6912-6920. Long beach, CA, USA: PMLR: Proceedings of Machine Learning Research. Related URL.
  • Kamary, K., Lee, J. E., & Robert, C. P. (2018). Weakly Informative Reparameterizations for Location-Scale Mixtures. Journal of Computational and Graphical Statistics, 1-13. 10.1080/10618600.2018.1438900

Contact details

Alternative contact

DDI +64 9 923 5237

Primary office location

Level 3, Room 383
New Zealand

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