Leo Lee is an associate professor of mathematics at the University of Mary Washington.
Areas of Expertise (6)
Sabbatical Award (professional)
Awarded by the University of Mary Washington.
Experiential Learning Grant (professional)
Awarded by the Center for Teaching Excellence and Innovation.
Professional Affiliation Development Grant (professional)
Awarded by the Korean-American Scientists and Engineers Association.
Computational Science and Engineering Session Grant (professional)
Awarded by the Korean Mathematical Society.
Iowa State University: Ph.D., Applied Mathematics 2008
Sogang University: M.S., Mathematics
Kangnam University: B.S., Mathematics
- Korean-American Mathematical Scientists Association
Media Appearances (1)
Fredericksburg-area health officials say 'worst is probably still ahead of us'
Stern has been working with Leo Lee, a math professor at the University of Mary Washington, to develop charts showing “the true number of new cases” locally, said health district spokesperson Allison Balmes–John.
Event Appearances (4)
Optimal Control Problems for SPDEs with Neumann Conditions
US-Korea Conference 2015 Atlanta, GA
A non-overlapping DDM for the numerical solution of stochastic elliptic PDEs
Mathematics Colloquium, Department of Mathematics, Sogang University Seoul, Korea
An Optimization Based Domain Decomposition Method for PDEs with Random Inputs
Joint Mathematics Meetings Baltimore, MD
How to Analyze Real-World Problems?
Mathematics Seminar, Department of Mathematics, Sogang University Seoul, Korea
An optimization based domain decomposition method for PDEs with random inputsComputers & Mathematics with Applications
2014 ABSTRACT: An optimization-based domain decomposition method for stochastic elliptic partial differential equations is presented. The main idea of the method is a constrained optimization problem for which the minimization of an appropriate functional forces the solutions on the two subdomains to agree on the interface; the constraints are the stochastic partial differential equations.
A Stochastic Galerkin Method for Stochastic Control ProblemsCommunications in Computational Physics
2013 ABSTRACT: In an interdisciplinary field on mathematics and physics, we examine a physical problem, fluid flow in porous media, which is represented by a stochastic partial differential equation (SPDE). We first give a priori error estimates for the solutions to an optimization problem constrained by the physical model under lower regularity assumptions than the literature. We then use the concept of Galerkin finite element methods to establish a new numerical algorithm to give approximations for our stochastic optimal physical problem.
Error Estimates of Stochastic Optimal Neumann Boundary Control ProblemsSIAM Journal on Numerical Analysis
2011 ABSTRACT: We study mathematically and computationally optimal control problems for stochastic partial differential equations with Neumann boundary conditions. The control objective is to minimize the expectation of a cost functional, and the control is of the deterministic, boundary-value type. Mathematically, we prove the existence of an optimal solution and of a Lagrange multiplier ...
A Robin-Robin non-overlapping domain decomposition method for an elliptic boundary control problemInternational Journal of Numerical Analysis & Modeling
2011 ABSTRACT: A Robin-Robin non-overlapping domain decomposition method for an optimal boundary control problem associated with an elliptic boundary value problem is presented. The existence of the whole domain and subdomain optimal solutions is proven. The convergence of the subdomain optimal solutions to the whole domain optimal solution is shown. The optimality system is derived and a gradient-type method is defined for finding the optimal solution. A theoretic convergence result for the gradient method is established. The finite element version of the Robin-Robin non-overlapping domain decomposition method is analyzed and some numerical results by the method on both serial and parallel computers (using MPI) are presented.