Hongjoong Kim

795 total citations
36 papers, 511 citations indexed

About

Hongjoong Kim is a scholar working on Finance, Statistical and Nonlinear Physics and Statistics, Probability and Uncertainty. According to data from OpenAlex, Hongjoong Kim has authored 36 papers receiving a total of 511 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Finance, 6 papers in Statistical and Nonlinear Physics and 6 papers in Statistics, Probability and Uncertainty. Recurrent topics in Hongjoong Kim's work include Stochastic processes and financial applications (6 papers), Nonlinear Waves and Solitons (4 papers) and Advanced Mathematical Physics Problems (4 papers). Hongjoong Kim is often cited by papers focused on Stochastic processes and financial applications (6 papers), Nonlinear Waves and Solitons (4 papers) and Advanced Mathematical Physics Problems (4 papers). Hongjoong Kim collaborates with scholars based in South Korea, Canada and United States. Hongjoong Kim's co-authors include Alexander G. Tartakovsky, B. L. Rozovskiĭ, Rudolf B. Blažek, Okan Şirin, Bouzid Choubane, M Linder Tia, Y. Kim, Wonjung Kim, Shin‐Wha Lee and Ji‐Young Lee and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Signal Processing and Construction and Building Materials.

In The Last Decade

Hongjoong Kim

32 papers receiving 494 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hongjoong Kim South Korea 9 161 145 144 100 88 36 511
Aleksey S. Polunchenko United States 9 190 1.2× 103 0.7× 129 0.9× 141 1.4× 57 0.6× 23 436
R. C. H. Cheng United Kingdom 14 143 0.9× 95 0.7× 173 1.2× 277 2.8× 29 0.3× 31 644
Enlu Zhou United States 13 87 0.5× 67 0.5× 140 1.0× 51 0.5× 83 0.9× 88 610
Kuang Zhou China 13 106 0.7× 31 0.2× 278 1.9× 32 0.3× 49 0.6× 34 588
Gerardo Rubino France 9 108 0.7× 67 0.5× 44 0.3× 95 0.9× 27 0.3× 18 474
Jacek Leśkow Poland 15 23 0.1× 32 0.2× 99 0.7× 72 0.7× 170 1.9× 42 501
Ryohei Fujimaki Japan 12 17 0.1× 97 0.7× 310 2.2× 40 0.4× 65 0.7× 40 505
Nadia Oudjane France 9 22 0.1× 47 0.3× 153 1.1× 51 0.5× 70 0.8× 30 363
Wen‐Xin Zhou United States 14 47 0.3× 28 0.2× 163 1.1× 399 4.0× 56 0.6× 48 686
Lipeng Pan China 12 50 0.3× 40 0.3× 308 2.1× 90 0.9× 81 0.9× 26 551

Countries citing papers authored by Hongjoong Kim

Since Specialization
Citations

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

Fields of papers citing papers by Hongjoong Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hongjoong Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Hongjoong Kim. A scholar is included among the top collaborators of Hongjoong Kim 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 Hongjoong Kim. Hongjoong Kim 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, Hongjoong, et al.. (2025). Markov regime-switching in pricing equity-linked securities: An empirical study for losses in HSCEI-linked products. Finance research letters. 76. 106929–106929. 1 indexed citations
2.
Kim, Hongjoong, et al.. (2024). Robust baseline correction for Raman spectra by constrained Gaussian radial basis function fitting. Chemometrics and Intelligent Laboratory Systems. 253. 105205–105205. 3 indexed citations
3.
Kang, Sung Wan, Ji‐Young Lee, Hongjoong Kim, et al.. (2024). Evaluation of the anti-cancer efficacy of lipid nanoparticles containing siRNA against HPV16 E6/E7 combined with cisplatin in a xenograft model of cervical cancer. PLoS ONE. 19(2). e0298815–e0298815. 16 indexed citations
4.
Kim, Hongjoong, et al.. (2022). Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm. Sensors. 22(9). 3244–3244. 5 indexed citations
5.
Kim, Hongjoong, et al.. (2022). Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence. Sensors. 22(20). 7982–7982. 2 indexed citations
6.
Kim, Hongjoong, et al.. (2022). Stock market prediction based on adaptive training algorithm in machine learning. Quantitative Finance. 22(6). 1133–1152. 9 indexed citations
7.
Kim, Hongjoong, et al.. (2018). Relaxation model for the p-Laplacian problem with stiffness. Journal of Computational and Applied Mathematics. 344. 173–189. 3 indexed citations
8.
Kim, Hongjoong, et al.. (2017). A prediction methodology for the change of the values of financial products. ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH. 51(3). 197–210.
9.
Kim, Hongjoong, et al.. (2016). The hyperbolic relaxation systems for the forced KdV equations with hydraulic falls. European Journal of Mechanics - B/Fluids. 58. 20–28. 1 indexed citations
10.
Kim, Hongjoong, et al.. (2013). An adaptive averaging binomial method for option valuation. Operations Research Letters. 41(5). 511–515. 3 indexed citations
11.
Kim, Hongjoong, et al.. (2012). Stability of Symmetric Solitary Wave Solutions of a Forced Korteweg-de Vries Equation and the Polynomial Chaos. Advances in Applied Mathematics and Mechanics. 4(6). 833–847. 2 indexed citations
12.
Kim, Hongjoong, et al.. (2011). Dependence of polynomial chaos on random types of forces of KdV equations. Applied Mathematical Modelling. 36(7). 3080–3093. 10 indexed citations
13.
Kim, Hongjoong, et al.. (2009). The High Order WENO Scheme on the Adaptive Mesh. 311–312.
14.
Kim, Wonjung, et al.. (2008). Adaptive lattice methods for multi-asset models. Computers & Mathematics with Applications. 56(2). 352–366. 10 indexed citations
15.
Kim, Hongjoong, et al.. (2007). ANALYTIC MODEL OF IEEE 802.15.4 Unslotted CSMA/CA. 3(2). 175–176. 1 indexed citations
16.
Tartakovsky, Alexander G., B. L. Rozovskiĭ, Rudolf B. Blažek, & Hongjoong Kim. (2006). A novel approach to detection of intrusions in computer networks via adaptive sequential and batch-sequential change-point detection methods. IEEE Transactions on Signal Processing. 54(9). 3372–3382. 187 indexed citations
17.
Kim, Hongjoong. (2006). An efficient computational method for statistical moments of Burger′s equation with random initial conditions. Mathematical Problems in Engineering. 2006(1). 2 indexed citations
18.
Kim, Hongjoong. (2006). Numerical solutions of Burgers’ equation with random initial conditions using the Wiener chaos expansion and the Lax–Wendroff scheme. Applied Mathematics Letters. 20(5). 545–550. 4 indexed citations
19.
Shin, Yong Kook, et al.. (1996). Microbial DNA base composition(G+C mol%) and its taxonomic implications. KRIBB Repository. 6(1). 72–77. 3 indexed citations
20.
Shin, Yong Kook, et al.. (1995). Isoprenoid Quinone Profiles in Microbial Taxonomy. KRIBB Repository. 5(4). 211–217. 5 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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