I am currently a quantitative researcher focusing on modeling problems in finance. I graduated from the University of Chicago Statistics Ph.D. program, and worked with Prof. Mihai Anitescu and Prof. Michael L. Stein. My thesis concentrates on computational and statistical methods for the optimal estimation, control and modeling of dynamical systems.
Ph.D. in Statistics, 2017
The University of Chicago
B.S. in Mathematics, minor in Statistics, 2013
The University of Hong Kong
Morgan Stanley, Quantitative Researcher, Aug 2017 - Present
Argonne National Laboratory, Research Intern, Jun - Aug 2014, 2015
Xu, W., Stein, M. L. and Wisher, I. 2018+. Modeling and predicting chaotic circuit data. SIAM/ASA Journal on Uncertainty Quantification, to appear.
Xu, W. and Anitescu, M. 2018. Exponentially accurate temporal decomposition for long-horizon linear-quadratic dynamic optimization. SIAM Journal on Optimization, 28(3): 2541-2573.
Xu, W. and Stein, M. L. 2017. Maximum likelihood estimation for a smooth Gaussian random field model. SIAM/ASA Journal on Uncertainty Quantification, 5(1): 138-175.
Xu, W. and Anitescu, M. 2016. A limited-memory multiple shooting method for weakly constrained data assimilation. SIAM Journal on Numerical Analysis, 54(6): 3300-3331.
Xu, W. and Anitescu, M. 2017. Exponentially convergent receding horizon strategy for constrained optimal control. Submitted.
A limited-memory multiple shooting method for weakly constrained variational data assimilation. SIAM Conference on Uncertainty Quantification, Lausanne, Switzerland, April 2016.
Limited-memory weakly constraint 4D-Var with checkpointing gradient. Summer Student Symposium, Argonne National Laboratory, Lemont, IL, August 2015.