Joong‐Ho Won
- Statistics and Probability top 5%
- Artificial Intelligence
- Molecular Biology
- Computational Mechanics
- Computer Vision and Pattern Recognition
- Co-authors
- Johan LimBala RajaratnamSungroh YoonMyunghee Cho PaikYongchan KwonBeom Joon KimRichard A. OlshenShai S. Shen-Orr
- Topics
- Sparse and Compressive Sensing Techniques (9 papers)Medical Image Segmentation Techniques (5 papers)Statistical Methods and Inference (5 papers)
- Partner nations
- South KoreaUnited StatesSingapore
In The Last Decade
Joong‐Ho Won
32 papers receiving 264 citations
Peers
Comparison fields: 5 of 96
- Statistics and Probability 70
- Artificial Intelligence 57
- Molecular Biology 48
- Computational Mechanics 35
- Computer Vision and Pattern Recognition 32
Countries citing papers authored by Joong‐Ho Won
This map shows the geographic impact of Joong‐Ho Won'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 Joong‐Ho Won with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joong‐Ho Won more than expected).
Fields of papers citing papers by Joong‐Ho Won
This network shows the impact of papers produced by Joong‐Ho Won. 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 Joong‐Ho Won. The network helps show where Joong‐Ho Won may publish in the future.
Co-authorship network of co-authors of Joong‐Ho Won
This figure shows the co-authorship network connecting the top 25 collaborators of Joong‐Ho Won. A scholar is included among the top collaborators of Joong‐Ho Won based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joong‐Ho Won. Joong‐Ho Won is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 0 | |
| 9 | Proximity Operator of the Matrix Perspective Function and its Applications | 2 |
| 10 | Projection onto Minkowski Sums with Application to Constrained Learning. | 6 |
| 11 | Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator | 2 |
| 12 | 1 | |
| 13 | 13 | |
| 14 | Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation | 31 |
| 15 | 3 | |
| 16 | 1 | |
| 17 | On a Class of First-order Primal-Dual Algorithms for Composite Convex Minimization Problems | 1 |
| 18 | 1 | |
| 19 | 30 | |
| 20 | 6 |
About Joong‐Ho Won
Joong‐Ho Won is a scholar working on Statistics and Probability, Computer Vision and Pattern Recognition and Numerical Analysis, having authored 37 papers that have together received 275 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (9 papers), Medical Image Segmentation Techniques (5 papers) and Statistical Methods and Inference (5 papers). The work is most often cited by research in Statistics and Probability (70 citations), Signal Processing (23 citations) and Biophysics (11 citations). Joong‐Ho Won has collaborated with scholars based in South Korea, United States and Singapore. Frequent co-authors include Johan Lim, Bala Rajaratnam, Sungroh Yoon, Myunghee Cho Paik, Yongchan Kwon, Beom Joon Kim, Richard A. Olshen, Shai S. Shen-Orr, Ofir Goldberger and Mark M. Davis. Their work appears in journals such as Proceedings of the National Academy of Sciences, PLoS ONE and IEEE Access.
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.