Jialing Han is an Assistant Professor in Actuarial Data Science at Heriot-Watt University. Her research focuses on dependence modelling, quantitative risk management, actuarial science and actuarial statistics, particularly in vine copulas, time series, factor models and GARCH processes.

She completed her PhD in Management with a specialisation in Actuarial Science at the University of York in 2023. Her research has applications in insurance and finance, including mortality modelling and forecasting, financial risk management and high-dimensional dependence modelling. She has published and submitted research in statistical and econometric journals, including the Canadian Journal of Statistics, Journal of Multivariate Analysis and Journal of Econometrics. Jialing has teaching experience in actuarial science, statistics, finance and quantitative methods.

I am passionate about dependence modelling and quantitative risk management, particularly their applications in actuarial science, insurance and finance.

At Heriot-Watt University, I am part of the actuarial and data science research community.

My current research focuses on three related themes:

• dependence modelling and time series – developing vine copula, S-vine and D-vine models for complex dependence structures and financial time series;
• risk management and actuarial modelling – applying advanced dependence models to financial risk, insurance and mortality modelling and forecasting;
• high-dimensional and non-stationary data – developing new statistical methods for factor models, GARCH processes and multivariate data, including the integration of dependence modelling with machine learning.