Click here to watch a talk I gave at the 2009 DOE CSGF Conference.
** This talk is suitable for a general science audience. **
Primary Research Interests:
 Scientific Computing and Numerical Linear Algebra
 Inverse Problems
 High Performance Computing
 Image Reconstruction
 Imaging Applications
As a computational scientist, my research lies in the intersection of three areas:
 mathematics,
 computer science, and
 scientific applications.
Some research projects:
Regularization for illposed inverse problems:
Most inverse problems are illposed, meaning small changes in the data can lead to large changes in the solution. Regularization is an approach to modify the problem and overcome this instability. My research on regularization includes spectral filtering, variational methods, hybrid approaches, and parameter selection methods.
Image Processing Applications:
Applications such as geophysics, molecular biology, astronomy, and medicine rely heavily on good imaging devices. Some problems that I work on include image deblurring, image denoising, and superresolution imaging. I also develop methods for tomographic imaging, where the goal is to reconstruct 3D images from 2D projection data.

Separable Nonlinear Problems:
Some problems have special structure, in that they are linear in some variables and nonlinear in others. In this project, we develop numerical methods that can efficiently solve such problems. Applications from superresolution and blinddeconvolution are considered.

Optimal Design of Filters:
In many applications, image reconstructions have to be done quickly and expensive parameter choice methods may not be possible. In this project, calibration data is used in an empirical Bayes risk minimization framework to precompute spectral filters.
Large Scale Problems:
Achieving high resolution images often requires highperformance computing capabilities. In my research, I am not only interested in developing numerical algorithms, but also efficient implementations for distributed architectures.
Software
Click here for my GitHub page
HyBR: Efficient implementations of GolubKahan based hybrid methods for solving illposed inverse problems.
Optimal Regularized Inverse Matrices (ORIMs): This repository contains MATLAB files for computing lowrank ORIMs and ORIM updates as described in the paper:
Julianne Chung and Matthias Chung. Optimal Regularized Inverse Matrices for Inverse Problems, arXiv:1511169, 2016.
Quantitative
Susceptibility Mapping (QSM) Reconstruction : This repository contains MATLAB files for image deblurring and Quantitative
Susceptibility Mapping used in the review paper:
J.Chung, L.Ruthotto, Computational Methods for Image Reconstruction, NMR Biomedicine Special Issue: QSM, 2016
Online Audio Recordings:
 Recent Advances in Numerical Linear Algebra for Inverse Problems Part I
 Recent Advances in Numerical Linear Algebra for Inverse Problems Part II
 A Celebration in Honor of Dianne P. O'Leary on the Occasion of her Retirement Part I
 A Celebration in Honor of Dianne P. O'Leary on the Occasion of her Retirement Part II
 A direct link to my talk: A Hybrid LSMR Algorithm for LargeScale Tikhonov Regularization
If you are an undergraduate or graduate student interested in research in any of these or related areas, please send me an email at jmchung@vt.edu. I am happy to meet with you to discuss research opportunities.
Many of my projects include interdisciplinary collaboration with scientists from various departments such as mathematics, computer science, radiology, and engineering. Please contact me if you are interested in potential collaborations.
Some Links
 AWM (Association for Women in Mathematics)
 SIAM (Society for Industrial and Applied Mathematics)
 AMS (American Mathematical Society)