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.

 

Teaching | Current

2020 S1 STATS 762 and STATS 201/208

2020 S2 STATS 331

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

Selected publications and creative works (Research Outputs)

  • 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
    URL: http://hdl.handle.net/2292/48329
  • 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.
    URL: http://hdl.handle.net/2292/48064
  • 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
    URL: http://hdl.handle.net/2292/45939
  • 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

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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