Professor Wang’s research interests include developing statistical methodology for correlated data analysis, robust inferences and model selection and applying advanced techniques that help to solve important problems in medical sciences, environmental research and natural resource management.
Currently, he is a member of ARC College of Experts, and on the Editorial Board of Biometrics and Environmental Modeling and Assessment. Professor Wang also has extensive experience in initiating research projects and experience in designing experiments, data analysis and working with environmental researchers, marine biologists and others. Professor Wang has been very successful in grant applications including FRDC (Fisheries Research and Development Corporation) and ARC DP and Linkage grants. He has experience in training students and researchers with effective courses and programs and engaging with government and industry partners. His work is usually motivated by practical need.
In statistics, Professor Wang is interested in developing methodologies in the main stream of statistics, particularly in the area of clustered/longitudinal data analysis to model the covariance and model selection criteria. His work has been published in Biometrika, Biometrics , Journal of the American Statistical Association, Statistical Methods in Medical Research, Annals of Statistics, Statistics in Medicine and Biostatistics.
In knowledge-based applied statistics, Professor Wang has extensive experience in initiating research projects and designing experiments, data analysis and working with environmental researchers, marine biologists. He contributed as main or sole author to many other disciplines including publications by Journal of Hydrology , Water Resources Research, Geophysical Research Letters, Envoironmental Science & Technology, Phytopathology, Fisheries etc.
Developing Statistical Methodology
Longitudinal Data Analysis
D.Phil (Statistics), Oxford
1991 Statistics D.Phil, University of Oxford (Supervised by Prof JC Gittins) 1988 Statistics M.Sc, Peking University 1986 Mathematics B.Sc, Zhejiang University
What if farmers could tell if one of their livestock was sick, without even being around the animal? Farmers are now using inertial motion sensors to study grazing behaviour. But the challenge is interpreting all the data those sensors provide. New research led by ACEMS PhD candidate Shuwen Hu reveals which machine learning methods are best suited to handle the problem.
Invited talks, refereed proceedings and other conference outputs
Hu, S., Li Y.., Ingham A.., Hurley G.. B., Gonzalez E.. G., & Wang Y-G.
(2019). Comparison of Machine Learning Algorithms in Classification of Grazing Behaviour in Sheep. The 20th INFORMS Applied Probability Society Conference.
Wang, Y-G., & Wu J.
(2019). Chaotic time series regression modeling using phase space reconstruction and deep neural network. The 20th INFORMS Applied Probability Society Conference.
Hu, S., & Wang Y-G.
(2020). Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours. Computers and Electronics in Agriculture.