Dr Kate Jeong Eun Lee
BTech (UoA), MSc (UoA), PhD (QUT)
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
- 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
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
Review panel of conference proceedings for
Selected publications and creative works (Research Outputs)
- 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
- 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.
- 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