Dr Kate Jeong Eun Lee

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

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Lecturer

Research | Current

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

My research interests include;

  • Bayesian statsitics
  • Monte Carlo methods
  • Mixtures
  • Inference using approixmations due to expensive posteiror distribuitons
  • 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

https://scholar.google.co.nz/citations?user=B5R1SvgAAAAJ&hl=en

 

Teaching | Current

2019 S1 STATS 762 

2019 S2 STATS 201/208 and STATS 331

Postgraduate supervision

PhD

2019-now, Yifu Tang

2019-now, Yixuan Liu 

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”

Areas of expertise

Bayesian analysis, Monte Carlo method, Mixtures, Extremes

Committees/Professional groups/Services

International Society of Bayesian Analysis
New Zealand Statistical Association

Review panel of ICML, NIPS, AISTATS

Selected publications and creative works (Research Outputs)

  • 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
  • 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
  • Rapson, C. J., Seet, B. C., Naeem, M. A., Lee, J. E., Al-Sarayreh, M., & Klette, R. (2018). Reducing the pain: A novel tool for efficient ground-truth labelling in images. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) Auckland, New Zealand: IEEE. 10.1109/IVCNZ.2018.8634750
    URL: http://hdl.handle.net/2292/45325
  • Kamary, K., & Lee, K. (2017). Ultimixt: Bayesian analysis of location-scale mixture models using a weakly informative prior [Computer software]. Related URL.
    URL: http://hdl.handle.net/2292/45448
  • Lee, J. E., & Robert, C. P. (2016). Importance Sampling Schemes for Evidence Approximation in Mixture Models. Bayesian Analysis, 11 (2), 573-597. 10.1214/15-BA970
  • Lee, J., Fan, Y., & Sisson, S. A. (2015). Bayesian threshold selection for extremal models using measures of surprise. Computational Statistics & Data Analysis, 85, 84-99. 10.1016/j.csda.2014.12.004
  • Chung, H., & Lee, K. (2015). Detecting de-lamination in composite beams using natural frequencies and the Bayesian inference. In M. J. Crocker, M. Pawelczyk, F. Pedrielli, E. Carletti, S. Luzzi (Eds.) 22nd International Congress on Sound and Vibration 2015 (ICSV 22), 2. Florence, Italy. Related URL.
    URL: http://hdl.handle.net/2292/45487
  • Chung, H., & Lee, K. (2014). Detecting defects in composite beams and plates using Bayesian inference. In P. Sas, D. Moens, H. Denayer (Eds.) Proceedings International Conference on Noise and Vibration Engineering, International Conference on Uncertainly in Structural Dynamics, 3727-3738. Leuven, Belgium: Katholieke Universiteit Leuven. Related URL.
    URL: http://hdl.handle.net/2292/45346

Contact details

Alternative contact

DDI +64 9 923 5237

Primary office location

SCIENCE CENTRE 303S - Bldg 303S
Level 3, Room 383
38 PRINCES ST
AUCKLAND CENTRAL
AUCKLAND 1010
New Zealand

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