Lei (Larry) Hua, Ph.D., A.S.A.
Larry obtained his bachelor's degree in finance from the University of Science and Technology of China (USTC) in 2002. After graduation, he started to work in the actuarial industry and became an Associate of the Society of Actuaries in 2004. After resigning from his assistant actuarial manager position at Ping An Insurance in 2006, Larry moved to Canada, where he earned a master's degree in actuarial science from the University of Calgary in 2008. In 2012, Larry earned his Ph.D. in statistics from the University of British Columbia, where he was honored as a Killam scholar and was a recipient of the prestigious national CGS-D3 award.
Larry's research interests are mainly motivated by applications, especially those from the financial and insurance industries. One emphasis of Larry's research has been on the tail behavior of multivariate non-Gaussian phenomena and its applications. Larry's current research interests are focused on advanced statistical and machine learning techniques and innovative applications to massive datasets.
Larry also advocates open-source software and textbook developments. Please refer to his Github account and website for his interest and contribution to the open source community.
Xu, M., Hua, L., and Xu, S. (2017). A vine copula model for predicting the effectiveness of cyber defense early-warning. Technometrics, 59, 508-520.
Su, J., and Hua, L., 2017. A general approach to full-range tail dependence copulas. Insurance: Mathematicics and Economics, 77, 49-64.
Hua, L., 2017. On a bivariate copula with both upper and lower full-range tail dependence, Insurance: Mathematics and Economics, 73, 94-104.