High-Performance Three-Dimensional Image Reconstruction for Molecular Structure Determination. - Julianne Chung; Philip Sternberg; Chao Yang 001 (Int. J. High Perform. Comput. Appl., 2010)

Numerical Algorithms for Polyenergetic Digital Breast Tomosynthesis Reconstruction. - Julianne Chung; James G. Nagy; Ioannis Sechopoulos (SIAM J. Imaging Sci., 2010)

An Efficient Iterative Approach for Large-Scale Separable Nonlinear Inverse Problems. - Julianne Chung; James G. Nagy (SIAM J. Sci. Comput., 2010)

Designing Optimal Spectral Filters for Inverse Problems. - Julianne Chung; Matthias Chung; Dianne P. O'Leary (SIAM J. Sci. Comput., 2011)

Windowed Spectral Regularization of Inverse Problems. - Julianne Chung; Glenn R. Easley; Dianne P. O'Leary (SIAM J. Sci. Comput., 2011)

Optimal Filters from Calibration Data for Image Deconvolution with Data Acquisition Error. - Julianne Chung; Matthias Chung; Dianne P. O'Leary (J. Math. Imaging Vis., 2012)

Computing optimal low-rank matrix approximations for image processing. - Julianne Chung; Matthias Chung (ACSSC, 2013)

An Efficient Approach for Computing Optimal Low-Rank Regularized Inverse Matrices. - Julianne Chung; Matthias Chung (CoRR, 2014)

A Framework for Regularization via Operator Approximation. - Julianne M. Chung; Misha E. Kilmer; Dianne P. O'Leary (SIAM J. Sci. Comput., 2015)

A Hybrid LSMR Algorithm for Large-Scale Tikhonov Regularization. - Julianne Chung; Katrina Palmer (SIAM J. Sci. Comput., 2015)

Large-Scale Inverse Problems in Imaging. - Julianne Chung; Sarah Knepper; James G. Nagy (Handbook of Mathematical Methods in Imaging, 2015)

Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems. - Julianne Chung; Matthias Chung; J. Tanner Slagel; Luis Tenorio (CoRR, 2017)

Motion Estimation and Correction in Photoacoustic Tomographic Reconstruction. - Julianne Chung; Linh Nguyen (SIAM J. Imaging Sci., 2017)

Optimal Regularized Inverse Matrices for Inverse Problems. - Julianne Chung; Matthias Chung (SIAM J. Matrix Anal. Appl., 2017)

Generalized Hybrid Iterative Methods for Large-Scale Bayesian Inverse Problems. - Julianne Chung; Arvind K. Saibaba (SIAM J. Sci. Comput., 2017)

Optimal Experimental Design for Inverse Problems with State Constraints. - Lars Ruthotto; Julianne Chung; Matthias Chung (SIAM J. Sci. Comput., 2018)

Iterative Sampled Methods for Massive and Separable Nonlinear Inverse Problems. - Julianne Chung; Matthias Chung; J. Tanner Slagel (SSVM, 2019)

Sampled Limited Memory Methods for Massive Linear Inverse Problems. - Julianne Chung; Matthias Chung; J. Tanner Slagel; Luis Tenorio (CoRR, 2019)

Flexible Krylov Methods for ℓp Regularization. - Julianne Chung; Silvia Gazzola (SIAM J. Sci. Comput., 2019)

Hybrid Projection Methods for Large-scale Inverse Problems with Mixed Gaussian Priors. - Taewon Cho; Julianne Chung; Jiahua Jiang (CoRR, 2020)

Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems. - Arvind K. Saibaba; Julianne Chung; Katrina Petroske (Numer. Linear Algebra Appl., 2020)

Learning Regularization Parameters of Inverse Problems via Deep Neural Networks. - Babak Maboudi Afkham; Julianne Chung; Matthias Chung (CoRR, 2021)

Efficient learning methods for large-scale optimal inversion design. - Julianne Chung; Matthias Chung; Silvia Gazzola; Mirjeta Pasha (CoRR, 2021)

Research in Inverse Problems and Training in Computational Science: A Reflection on the Importance of Community. - Julianne Chung (Comput. Sci. Eng., 2021)

Hybrid Projection Methods with Recycling for Inverse Problems. - Jiahua Jiang; Julianne Chung; Eric de Sturler (SIAM J. Sci. Comput., 2021)

slimTrain - A Stochastic Approximation Method for Training Separable Deep Neural Networks. - Elizabeth Newman; Julianne Chung; Matthias Chung; Lars Ruthotto (SIAM J. Sci. Comput., 2022)

Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks. - Babak Maboudi Afkham; Julianne Chung; Matthias Chung (CoRR, 2023)

Flexible Krylov Methods for Group Sparsity Regularization. - Julianne Chung; Malena Sabaté Landman (CoRR, 2023)

Paired Autoencoders for Inverse Problems. - Matthias Chung; Emma Hart; Julianne Chung; Bas Peters; Eldad Haber (CoRR, 2024)

Inner Product Free Krylov Methods for Large-Scale Inverse Problems. - Ariana N. Brown; Julianne Chung; James G. Nagy; Malena Sabaté Landman (CoRR, 2024)

Inexact Generalized Golub-Kahan Methods for Large-Scale Bayesian Inverse Problems. - Yutong Bu; Julianne Chung (CoRR, 2024)

Efficient hyperparameter estimation in Bayesian inverse problems using sample average approximation. - Julianne Chung; Scot M. Miller; Malena Sabate Landman; Arvind K. Saibaba (CoRR, 2024)

Computational Methods for Large-Scale Inverse Problems: A Survey on Hybrid Projection Methods. - Julianne Chung; Silvia Gazzola (SIAM Rev., 2024)

Hybrid Projection Methods for Solution Decomposition in Large-Scale Bayesian Inverse Problems. - Julianne Chung; Jiahua Jiang; Scot M. Miller; Arvind K. Saibaba (SIAM J. Sci. Comput., 2024)

Efficient iterative methods for hyperparameter estimation in large-scale linear inverse problems. - Khalil A Hall-Hooper; Arvind K. Saibaba; Julianne Chung; Scot M. Miller (Adv. Comput. Math., 2024)

Iterative Reconstruction Methods for Cosmological X-Ray Tomography. - Julianne Chung; Lucas Onisk; Yiran Wang (SIAM J. Imaging Sci., 2025)

A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization. - Emma Hart; Julianne Chung; Matthias Chung (CoRR, 2025)

Randomized and Inner-product Free Krylov Methods for Large-scale Inverse Problems. - Malena Sabaté Landman; Ariana N. Brown; Julianne Chung; James G. Nagy (CoRR, 2025)

Efficient sampling approaches based on generalized Golub-Kahan methods for large-scale hierarchical Bayesian inverse problems. - Elle Buser; Julianne Chung (CoRR, 2025)

Reconstruction of the observable universe from the integrated Sachs-Wolfe effect. - Julianne Chung; Yiran Wang (CoRR, 2025)