Victor Churchill

Engineering · The Ohio State University

Early career · publishing since 2018

Sparse and Compressive Sensing Techniques Model Reduction and Neural Networks Gaussian Processes and Bayesian Inference Statistical Methods and Inference Neural Networks and Applications

Publications

Citations

164

Est. group size

Recurring co-author estimate

Active years

Publishing since 2018

Research summary

AI-generated

Victor Churchill works on computational and applied mathematics methods for reconstructing signals and images from incomplete, noisy, or limited data, with a strong focus on quantifying uncertainty in these reconstructions. His work spans synthetic aperture radar (SAR) imaging, tomographic (CT) image reconstruction, edge detection, and learning models of physical processes (partial differential equations) from sparse data, often using Bayesian statistical techniques.

Uncertainty quantification in imagingSignal and image reconstruction from limited/noisy dataSynthetic aperture radar (SAR) imagingBayesian and sparse learning methodsData-driven modeling of physical systems (PDE learning)

Publication output has fluctuated over the last decade, rising to a peak in 2022 and continuing at a moderate, steady pace of about 4 papers per year over the last 5 years.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence

Publications per year over the last 10 years — averaging 4.0/year recently

Recent publications

Principal component flow map learning of PDEs from incomplete, limited, and noisy data
Journal of Computational Physics · 2025
Principal Component Flow Map Learning of PDEs from Incomplete, Limited, and Noisy Data
arXiv (Cornell University) · 2024
Sub-aperture SAR imaging with uncertainty quantification
Inverse Problems · 2023
Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification
SIAM/ASA Journal on Uncertainty Quantification · 2022
Estimation and Uncertainty Quantification for Piecewise Smooth Signal Recovery
Journal of Computational Mathematics · 2022
Sub-aperture SAR Imaging with Uncertainty Quantification
arXiv (Cornell University) · 2022
Synthetic Aperture Radar Image Formation with Uncertainty Quantification
arXiv (Cornell University) · 2020
Estimation and uncertainty quantification for piecewise smooth signal recovery
arXiv (Cornell University) · 2020
Detecting Edges from Non-uniform Fourier Data via Sparse Bayesian Learning
Journal of Scientific Computing · 2019
Edge-adaptive <inline-formula><tex-math id="M1">$ \ell_2 $</tex-math></inline-formula> regularization image reconstruction from non-uniform Fourier data
Inverse Problems and Imaging · 2019
Total Variation Bayesian Learning via Synthesis
arXiv (Cornell University) · 2019
Edge-masked CT image reconstruction from limited data
2019
Image reconstruction enhancement via masked regularization
arXiv (Cornell University) · 2019
Use of convexity in contour detection
arXiv (Cornell University) · 2019
Edge-adaptive l2 regularization image reconstruction from non-uniform Fourier data
arXiv (Cornell University) · 2018

Publishes in

arXiv (Cornell University)×16
Journal of Machine Learning for Modeling and Computing×5
Journal of Computational Physics×4
SSRN Electronic Journal×2
Journal of Scientific Computing×1

Similar researchers

By research-topic overlap

Rajiv Khanna
Engineering · Purdue University West Lafayette
David Tucker
Engineering · The Ohio State University
Chuyang Ke
Engineering · Purdue University West Lafayette
Chih-Hao Fang
Engineering · Purdue University West Lafayette
Kananart Kuwaranancharoen
Engineering · Purdue University West Lafayette

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 19, 2026.

Claim or correct this profile