Sanghun Choi

1.5k total citations
72 papers, 879 citations indexed

About

Sanghun Choi is a scholar working on Pulmonary and Respiratory Medicine, Computational Mechanics and Physiology. According to data from OpenAlex, Sanghun Choi has authored 72 papers receiving a total of 879 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Pulmonary and Respiratory Medicine, 15 papers in Computational Mechanics and 12 papers in Physiology. Recurrent topics in Sanghun Choi's work include Inhalation and Respiratory Drug Delivery (25 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (20 papers) and Asthma and respiratory diseases (12 papers). Sanghun Choi is often cited by papers focused on Inhalation and Respiratory Drug Delivery (25 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (20 papers) and Asthma and respiratory diseases (12 papers). Sanghun Choi collaborates with scholars based in South Korea, United States and China. Sanghun Choi's co-authors include Eric A. Hoffman, Ching‐Long Lin, Mario Castro, Sally E. Wenzel, Jiwoong Choi, Ching-Long Lin, Sean B. Fain, Chang Hyun Lee, Gong Yong Jin and Nizar N. Jarjour and has published in prestigious journals such as The Science of The Total Environment, Scientific Reports and Journal of Computational Physics.

In The Last Decade

Sanghun Choi

61 papers receiving 858 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sanghun Choi South Korea 18 510 163 153 120 94 72 879
Hiroko Kitaoka Japan 12 645 1.3× 36 0.2× 82 0.5× 47 0.4× 56 0.6× 49 891
Juerg Tschirren United States 16 934 1.8× 185 1.1× 33 0.2× 264 2.2× 96 1.0× 32 1.3k
Andrew Comerford Germany 15 280 0.5× 72 0.4× 80 0.5× 26 0.2× 120 1.3× 31 1.0k
Alberto Gambaruto United Kingdom 15 277 0.5× 89 0.5× 53 0.3× 56 0.5× 90 1.0× 38 771
Nicolas Buchmann Australia 17 166 0.3× 33 0.2× 46 0.3× 36 0.3× 107 1.1× 43 838
Catalin Fetita France 13 359 0.7× 86 0.5× 13 0.1× 107 0.9× 56 0.6× 60 523
Masayuki Tanabe Japan 12 128 0.3× 8 0.0× 107 0.7× 116 1.0× 198 2.1× 67 765
Hadrien Calmet Spain 12 269 0.5× 59 0.4× 86 0.6× 7 0.1× 35 0.4× 26 591
Andrea Borsic United States 17 88 0.2× 208 1.3× 1.3k 8.3× 91 0.8× 673 7.2× 45 1.5k
J. P. Whiteley United Kingdom 13 115 0.2× 20 0.1× 48 0.3× 103 0.9× 189 2.0× 43 602

Countries citing papers authored by Sanghun Choi

Since Specialization
Citations

This map shows the geographic impact of Sanghun Choi'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 Sanghun Choi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanghun Choi more than expected).

Fields of papers citing papers by Sanghun Choi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sanghun Choi. 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 Sanghun Choi. The network helps show where Sanghun Choi may publish in the future.

Co-authorship network of co-authors of Sanghun Choi

This figure shows the co-authorship network connecting the top 25 collaborators of Sanghun Choi. A scholar is included among the top collaborators of Sanghun Choi 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 Sanghun Choi. Sanghun Choi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kim, Chang‐Hyun, et al.. (2025). A Reproducible Benchmark for Gas Sensor Array Classification: From FE-ELM to ROCKET and TS2I-CNNs. Sensors. 25(20). 6270–6270.
2.
Park, Sujin, et al.. (2025). Physics-aware machine learning for computational fluid dynamics surrogate model to estimate ventilation performance. Physics of Fluids. 37(2). 2 indexed citations
3.
Chae, Kum Ju, et al.. (2025). Multiscale simulation of respiratory airflow using physiologically consistent geometry and boundary conditions in OpenFOAM. Computers in Biology and Medicine. 198(Pt A). 111131–111131.
4.
Kim, Woo Jin, et al.. (2025). Quantitative computed tomography imaging classification of cement dust-exposed patients-based Kolmogorov-Arnold networks. Artificial Intelligence in Medicine. 167. 103166–103166.
5.
6.
Sim, Da Woon, Sanghun Choi, Suh‐Young Lee, et al.. (2024). Computed tomography-based measurements associated with rapid lung function decline in severe asthma. Annals of Allergy Asthma & Immunology. 134(3). 306–314.e5. 2 indexed citations
7.
Ge, Haiwen, Hui Tang, Wenguo Weng, et al.. (2024). Assessing respiratory airflow unsteadiness under different tidal respiratory frequencies using large eddy simulation method. Computers in Biology and Medicine. 179. 108834–108834. 2 indexed citations
8.
Cui, Xinguang, Ching‐Long Lin, Stephen Baek, et al.. (2024). Human-airway surface mesh smoothing based on graph convolutional neural networks. Computer Methods and Programs in Biomedicine. 246. 108061–108061. 4 indexed citations
9.
10.
Jung, Jae‐Hyun, et al.. (2024). Numerical Assessment of Flow-Induced Vibration in Main Steam Piping Using Computational Fluid Dynamics (CFD). Transactions of the Korean Society of Mechanical Engineers B. 48(3). 193–202. 1 indexed citations
11.
Ge, Haiwen, et al.. (2023). Large eddy simulation study of the airflow characteristics in a human whole-lung airway model. Physics of Fluids. 35(7). 17 indexed citations
12.
Kim, Woo Jin, et al.. (2023). An unsupervised image registration method employing chest computed tomography images and deep neural networks. Computers in Biology and Medicine. 154. 106612–106612. 23 indexed citations
13.
Kim, Munho & Sanghun Choi. (2023). Numerical investigation of condensation-induced water hammer effects in horizontal and vertical cold reheat pipes. International Journal of Heat and Mass Transfer. 207. 124030–124030. 9 indexed citations
14.
Park, Eun‐Kee, et al.. (2023). Computed tomography-based imaging biomarker identifies coal workers’ pneumoconiosis. Frontiers in Physiology. 14. 1288246–1288246. 1 indexed citations
15.
Ge, Haiwen, et al.. (2022). Effects of face shield on an emitter during a cough process: A large-eddy simulation study. The Science of The Total Environment. 831. 154856–154856. 3 indexed citations
16.
Chae, Kum Ju, Gong Yong Jin, Jiwoong Choi, et al.. (2021). Generation-based study of airway remodeling in smokers with normal-looking CT with normalization to control inter-subject variability. European Journal of Radiology. 138. 109657–109657. 10 indexed citations
17.
Park, Byunggeon, Jaehee Lee, Jin Young Kim, et al.. (2021). Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study. JMIR Medical Informatics. 9(1). e24973–e24973. 25 indexed citations
18.
Kim, Chang‐Hyun, et al.. (2020). A Fundamental Study on Estimating the Concentration of Carbon Monoxide (CO) By Fabric-Based Colorimetric Analysis Using an Optical RGB Color Sensor. ECS Meeting Abstracts. MA2020-02(68). 3667–3667. 1 indexed citations
19.
Choi, Jiwoong, Lawrence J. LeBlanc, Sanghun Choi, et al.. (2019). Differences in Particle Deposition Between Members of Imaging-Based Asthma Clusters. Journal of Aerosol Medicine and Pulmonary Drug Delivery. 32(4). 213–223. 24 indexed citations
20.
Choi, Sanghun, Eric A. Hoffman, Sally E. Wenzel, Mario Castro, & Ching-Long Lin. (2014). Improved CT-based estimate of pulmonary gas trapping accounting for scanner and lung-volume variations in a multicenter asthmatic study. Journal of Applied Physiology. 117(6). 593–603. 27 indexed citations

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.

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