Rate My Professor Ting Wang

Wang's research centres on the development of novel statistical models bridging statistics and geophysics, with expertise in point process theory, hidden Markov models, stochastic processes, and survival analysis. Her applications focus on forecasting earthquakes, slow slip events, volcanic eruptions, and geomagnetic storms. Key publications include Wang et al., "Earthquake forecasting from paleoseismic records," Nature Communications (2024); Allen and Wang, "Hidden Markov models for low-frequency earthquake recurrence," The American Statistician (2024); Wang et al., "Forecasting eruptions at poorly known volcanoes using analogs and multivariate renewal processes," Geophysical Research Letters (2022); Wang et al., "Bayesian modelling of marked point processes with incomplete records: Volcanic eruptions," Journal of the Royal Statistical Society: Series C (2020); and Buckby et al., "Model checking for hidden Markov models," Journal of Computational and Graphical Statistics (2020). In 2022, she was awarded $1 million from the MBIE Smart Ideas fund to create a probabilistic forecasting tool for great earthquakes, collaborating with experts from Otago, Massey, Victoria universities, and GNS Science. She has led projects funded by the Marsden Fund, MBIE Endeavour, EQC, and National Science Challenges. Her accolades include the 2022 Littlejohn Research Award, the New Zealand Statistical Association's premier research award; the 2017 University of Otago Early Career Award for Distinction in Research; and the 2013 Worsley Early Career Research Award from the New Zealand Statistical Association.