I am generally interested in exploiting functional and harmonic analysis, in combination with numerical analysis, in order to construct rigorous numerical approximations that can serve as the foundation for fast and efficient computational methods in physical sciences. Specific topics that I am interested in include quadrature rules, numerical linear algebra, structured matrix algorithms, and approximation theory for deep neural networks.

** Papers**

- M. V. de Hoop, M. Lassas, C. Wong, "Generalization and regularization in deep learning for nonlinear inverse problems", NeurIPS Workshop on Integration of Deep Learning Theories, 2018. (preprint)

- P. Caday, M. V. de Hoop, M. Lassas, C. Wong, "Deep neural networks learning to solve nonlinear inverse problems for the wave equation," 2018. (preprint)

- C. Wong, "Bilinear quadratures for inner products," SIAM J. Sci. Comput., 2016.

- A.C. Hansen & C. Wong, "On the computation of spectra and pseudospectra of self-adjoint and non-self-adjoint Schrodinger operators."

** Talks and Presentations**

- Going beyond HSS: Investigating the structure of
impedance operators for high frequency Helmholtz problems, GMIG 2017 Annual Review, 28 April 2017

- Theory and Computation for Bilinear Quadratures, SIAM Conference on Computational Science and Engineering, 15 March 2015

- New Convergence Estimates for Block Lanczos Methods for the Truncated SVD, 14 May 2014

**Codes**

All code can be found on my GitHub page.