Julianne Chung
- Mathematical Physics top 5%
- Numerical methods in inverse problems 20
- Computational Mathematics top 10%
- Computational Mechanics top 5%
- Sparse and Compressive Sensing Techniques 17
- Applied Mathematics top 5%
- Statistical and numerical algorithms 8
-
- Image and Signal Denoising Methods 5
- Advanced Image Processing Techniques 3
-
- Gaussian Processes and Bayesian Inference 4
-
- Matrix Theory and Algorithms 4
-
- Photoacoustic and Ultrasonic Imaging 2
- Co-authors
- James G. NagyMatthias ChungDianne P. O’LearyEldad HaberJianjun ChangKimberly C. PaulBeate RitzStewart A. Factor
- Partner nations
- United StatesUnited KingdomDenmark
In The Last Decade
Julianne Chung
33 papers receiving 547 citations
Peers
Comparison fields: 5 of 93
- Mathematical Physics 172
- Computational Mathematics 9
- Computational Mechanics 188
- Neurology 62
- Applied Mathematics 70
Countries citing papers authored by Julianne Chung
This map shows the geographic impact of Julianne Chung's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Julianne Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julianne Chung more than expected).
Fields of papers citing papers by Julianne Chung
This network shows the impact of papers produced by Julianne Chung. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Julianne Chung. The network helps show where Julianne Chung may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Julianne Chung, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 13 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 6 | |
| 5 | 2022 | 7 | |
| 6 | 2022 | 2 | |
| 7 | 2022 | 4 | |
| 8 | Learning regularization parameters of inverse problems via deep neural networks:Paper | 2021 | 29 |
| 9 | 2021 | 2 | |
| 10 | 2020 | 4 | |
| 11 | 2020 | 10 | |
| 12 | 2018 | 10 | |
| 13 | 2017 | 16 | |
| 14 | 2015 | 105 | |
| 15 | 2015 | 13 | |
| 16 | 2015 | 25 | |
| 17 | 2014 | 8 | |
| 18 | 2011 | 9 | |
| 19 | 2010 | 47 | |
| 20 | A weighted-GCV method for Lanczos-hybrid regularization. | 2007 | 99 |
About Julianne Chung
Julianne Chung is a scholar working on Mathematical Physics, Computational Mechanics and Applied Mathematics, having authored 34 papers that have together received 592 indexed citations. Recurring topics across this work include Numerical methods in inverse problems (20 papers), Sparse and Compressive Sensing Techniques (17 papers), Statistical and numerical algorithms (8 papers), Image and Signal Denoising Methods (5 papers), Gaussian Processes and Bayesian Inference (4 papers), Matrix Theory and Algorithms (4 papers), Advanced Image Processing Techniques (3 papers) and Photoacoustic and Ultrasonic Imaging (2 papers). The work is most often cited by research in Mathematical Physics (172 citations), Computational Mathematics (9 citations) and Computational Mechanics (188 citations). Julianne Chung has collaborated with scholars based in United States, United Kingdom and Denmark. Frequent co-authors include James G. Nagy, Matthias Chung, Dianne P. O’Leary, Eldad Haber, Jianjun Chang, Kimberly C. Paul, Beate Ritz, Stewart A. Factor, Jae‐Kyung Lee and George T. Kannarkat.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.