Dr Kate Jeong Eun Lee

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

Lecturer

In: Statistics » Faculty of Science

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

Committees/Professional groups/Services

Organizer of Bayesian Research Group meeting. Subscribe/unsubscribe the BRG emailing list from here.

Member of

International Society of Bayesian Analysis

New Zealand Statistical Association

LISA Consortium

Review panel of conference proceedings for

International Conference on Machine Learning (ICML)

Conference on Neural Information Processing Systems (NeurIPS)

International Conference on Artificial Intelligence and Statistics (AISTATS)

Association for the Advancement of Artificial Intelligence (AAAI)

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