Jun Fan
Impact in
- Statistics and Probability top 0.5%
- Statistical Methods and Inference
- Advanced Statistical Methods and Models
- Statistical Methods and Bayesian Inference
- Internal Medicine top 5%
Papers in
-
- Atrial Fibrillation Management and Outcomes 22
- Cardiac Arrhythmias and Treatments 13
- Cardiac electrophysiology and arrhythmias 6
-
- Statistical Methods and Inference 8
- Co-authors
- Irène GijbelsHans‐Georg MüllerMintu P. TurakhiaSusan SchmittChi Harold LiuKin K. LeungClaire T. ThanDing‐Xuan Zhou
- Journals
- Analysis and Applications (5 papers)Journal of the American College of Cardiology (5 papers)Journal of Complexity (2 papers)Journal of Fourier Analysis and Applications (2 papers)Applied and Computational Harmonic Analysis (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Jun Fan
83 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 160
- Statistics and Probability 560
- Internal Medicine 99
- Cardiology and Cardiovascular Medicine 548
- Computational Mechanics 234
- Artificial Intelligence 361
Countries citing papers authored by Jun Fan
This map shows the geographic impact of Jun Fan'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 Jun Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Fan more than expected).
Fields of papers citing papers by Jun Fan
This network shows the impact of papers produced by Jun Fan. 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 Jun Fan. The network helps show where Jun Fan may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Fan, 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 | 3 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 15 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 2 | |
| 8 | 2023 | 8 | |
| 9 | 2023 | 1 | |
| 10 | 2022 | 0 | |
| 11 | 2021 | 11 | |
| 12 | 2019 | 7 | |
| 13 | 2019 | 10 | |
| 14 | 2019 | 4 | |
| 15 | 2018 | 9 | |
| 16 | Sparsity and error analysis of empirical feature-based regularization schemes | 2016 | 3 |
| 17 | 2016 | 72 | |
| 18 | 2015 | 143 | |
| 19 | 2015 | 10 | |
| 20 | 2015 | 11 |
About Jun Fan
Jun Fan is a scholar working on Cardiology and Cardiovascular Medicine, Statistics and Probability, Internal Medicine, Computational Mechanics and Artificial Intelligence, having authored 91 papers that have together received 2.1k indexed citations. Recurring topics across this work include Atrial Fibrillation Management and Outcomes (22 papers), Sparse and Compressive Sensing Techniques (15 papers), Cardiac Arrhythmias and Treatments (13 papers), Neural Networks and Applications (8 papers), Statistical Methods and Inference (8 papers), Face and Expression Recognition (6 papers), Control Systems and Identification (6 papers) and Cardiac electrophysiology and arrhythmias (6 papers). The work is most often cited by research in Statistics and Probability (560 citations), Internal Medicine (99 citations), Cardiology and Cardiovascular Medicine (548 citations), Computational Mechanics (234 citations) and Artificial Intelligence (361 citations). Jun Fan has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Irène Gijbels, Hans‐Georg Müller, Mintu P. Turakhia, Susan Schmitt, Chi Harold Liu, Kin K. Leung, Claire T. Than, Ding‐Xuan Zhou, Ting Hu and Paul A. Heidenreich. Their work appears in journals such as Analysis and Applications, Journal of the American College of Cardiology, Journal of Complexity, Journal of Fourier Analysis and Applications and Applied and Computational Harmonic Analysis.
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.