I am a professor of Mathematics in the College of Science at Virginia Tech. I am also a member of the Interdisciplinary Center for Applied Mathematics.

I will teach two sections of Linear Algebra I MATH 3144_85855 and MATH 3144_85853 in the fall of 2018 and Numerical Analysis II, MATH 5466 in the spring of 2019.

3144: This course introduces more of the concepts behind linear algebra than simple matrix/vector operations, linear system solutions and the mechanics of eigenvalue problems. To fully understand these concepts and begin to extrapolate these ideas to novel settings, students will develop their own proofs of many of the fundamental themes in linear algebra and gain confidence in their ability to reason mathematically. These include vector spaces, linear transformations, their matrix representations, and decompositions of these transformations, as well as inner product spaces and the spectral theorem.

5465: The first semester of our Numerical Analysis sequence emphasizes error analysis for numerical linear algebra including linear systems and eigenvalue problems.

5466: The second semeseter of Numerical Analysis emphasizes elements
of *approximation theory* such as approximation by polynomials,
quadrature, as well as nonlinear equations and optimization and an
overview of numerical methods for ordinary differential equations.
MATH 5465 is not a prerequisite, but students should be familiar with
analysis and implementation of numerical algorithms.

My research is in the development and analysis of computational tools for optimal design and control of nonlinear PDEs (for example, active and passive control of fluid flows modeled by the Navier-Stokes equations). This research requires developments in a wide range of mathematical topics. A partial list is given below.

- Sensitivity Analysis:
- Adaptive Finite Element Calculations,
- Automatic Differentiation,
- Local Improvements in Reduced-Order Models,
- Nearby Solutions,
- Stability Analysis,
- Uncertainty Quantification

- Optimal Design Algorithms:
- Convergence Theory Based on Asymptotic Consistency,
- Forebody Simulator Design Problem,
- Parameter Estimation

- Computational Methods for Control of PDEs:
- Approximation to PDE Riccati Equations,
- Data Assimilation,
- Optimal Actuator and Sensor Placement

- Model Reduction Methods:
- Principal Interval Decomposition,
- Proper Orthogonal Decomposition,
- Extensions to Parameter Dependent Models,
- Extensions to Complex Turbulent Flows,
- Applications to Fluid Flow Control

- Applied Mathematics / Computational Science:
- Design of Energy Efficient Buildings,
- Computational Fluid Dynamics,
- Modeling for Ventilation in Mines,
- Optimal Zonation for Groundwater Models

A more complete description of my research can be found here.