Dr. Victor Churchill is a computational mathematician working in the areas of scientific machine learning and image reconstruction. His Ph.D., which focused on a Bayesian uncertainty quantification framework for synthetic aperture radar imaging, was supervised by Dr. Anne Gelb at Dartmouth College. His post-doctoral study was supervised by Dr. Dongbin Xiu at The Ohio State University and concentrated on learning the evolution of unknown dynamical systems using neural networks.

curriculum vitae

• Churchill, V., Manns, S., Chen, Z., and Xiu, D. (2023). Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged Learning, Journal of Computational Physics, 474, 111842.
• Churchill, V., and Gelb, A. (2023). Sub-Aperture SAR Imaging with Uncertainty Quantification, Inverse Problems, 39(5), 05400
• Chen, Z., Churchill, V., Wu, K., and Xiu, D. (2022). Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space, Journal of Computational Physics, 449, 110782.