Quantitative Methods for Finance

Professor Alcock's research explores the pricing and management of convexity in finance and real estate, with a particular interest in convex dependence structures.

Dr Huang's primary areas of research involve stochastic analysis and stochastic partial differential equations.

Dr Jiang's research interests are: inverse problems and imaging; model order reduction; uncertainty quantification.

Dr Leppinen has extensive experience applying mathematics to examine industrial processes. He is currently studying the dynamics of free surface flows, particularly the compressible and incompressible dynamics of bubbles and droplets.

Dr Li works in environmental and natural resource economics, and applied econometrics.

Dr Li has research interests in scientific computing, computational statistics and uncertainty quantification.

Dr Sachs's research interests are in numerical analysis of stochastic differential equations; computational statistics, the design and analysis of Markov Chain Monte Carlo methods; machine learning methods for molecular systems/dynamics.

Dr Shang's primary research interests lie in the optimal design of numerical methods for stochastic differential equations with a strong emphasis on applications ranging from computational mathematics, statistics, physics, to data science.

Dr Shao's principal research interests are in the intersection of financial and actuarial mathematics: quantitative analysis of Insurance-Linked Securities (ILS), Extreme Value Theory (EVT) and heavy-tailed distributions, with an emphasis on pricing catastrophe risk (CAT) bonds, nuclear power-linked securities and applications.

Dr Wang's primary research area is mathematical analysis of nonlinear dispersive PDEs, with tools from harmonic analysis, probability theory, and dynamical systems. In particular, he is interested in Strichartz estimates and its applications to dispersive nonlinear PDEs, probabilistic aspects of nonlinear dispersive PDEs, and normal form method applied to nonlinear dispersive PDEs.