Dangxing Chen is a Postdoctoral Scholar at the Consortium for Data Analytics in Risk at
the University of California, Berkeley. There, his research concentrates on the study of the
mathematical model and the development of efficient numerical methods in finance. His ongoing
focus is on the understanding of the asset return distribution from the continuous-time
stochastic volatility model perspective and the efficient numerical methods to the stochastic
differential equations.
Dangxing obtained his Ph.D. in Applied Mathematics from the University of North Carolina
at Chapel Hill in 2017 and a B.S. in Applied Mathematics from the University of Michigan
at Ann Arbor in 2013. During his period of undergraduate and graduate studies, he also
conducted research in the Lawrence Berkeley Laboratory, the University of Michigan at Ann
Arbor, and George Mason University. His thesis work centered on developing fast and accurate
numerical algorithms for partial differential equations and integral equations arisen
in science and engineering with the emphasis of the large-scale long-time complicated simulation.
His research has been applied to many fields, including time-dependent density-functional
theory, electromagnetics, and molecular dynamics.
