Hee‐Seok Oh

1.5k total citations
91 papers, 989 citations indexed

About

Hee‐Seok Oh is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Statistics and Probability. According to data from OpenAlex, Hee‐Seok Oh has authored 91 papers receiving a total of 989 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 21 papers in Control and Systems Engineering and 18 papers in Statistics and Probability. Recurrent topics in Hee‐Seok Oh's work include Image and Signal Denoising Methods (21 papers), Machine Fault Diagnosis Techniques (14 papers) and Advanced Statistical Methods and Models (13 papers). Hee‐Seok Oh is often cited by papers focused on Image and Signal Denoising Methods (21 papers), Machine Fault Diagnosis Techniques (14 papers) and Advanced Statistical Methods and Models (13 papers). Hee‐Seok Oh collaborates with scholars based in South Korea, United States and Canada. Hee‐Seok Oh's co-authors include Yunfei Chen, Donghoh Kim, Yongdai Kim, Hosik Choi, Thomas C. M. Lee, Douglas Nychka, Yaeji Lim, Doug Nychka, Ta‐Hsin Li and Youngjo Lee and has published in prestigious journals such as Journal of the American Statistical Association, IEEE Transactions on Pattern Analysis and Machine Intelligence and Technometrics.

In The Last Decade

Hee‐Seok Oh

78 papers receiving 946 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hee‐Seok Oh South Korea 16 269 190 155 142 140 91 989
Ery Arias-Castro United States 19 317 1.2× 154 0.8× 117 0.8× 541 3.8× 57 0.4× 60 1.5k
David Luengo Spain 21 287 1.1× 122 0.6× 117 0.8× 702 4.9× 133 0.9× 85 1.4k
Laurie Davies Germany 10 347 1.3× 97 0.5× 57 0.4× 147 1.0× 87 0.6× 26 866
Mohsen Pourahmadi United States 17 524 1.9× 41 0.2× 50 0.3× 247 1.7× 102 0.7× 64 1.3k
Fredrik Lindsten Sweden 19 143 0.5× 65 0.3× 63 0.4× 637 4.5× 341 2.4× 63 1.1k
George R. Terrell United States 13 631 2.3× 72 0.4× 43 0.3× 503 3.5× 213 1.5× 26 1.5k
Christian Tiberius Netherlands 28 67 0.2× 525 2.8× 114 0.7× 241 1.7× 67 0.5× 113 2.5k
Debashis Paul United States 15 734 2.7× 220 1.2× 179 1.2× 377 2.7× 34 0.2× 42 1.7k
Simon Watts United Kingdom 23 88 0.3× 263 1.4× 210 1.4× 439 3.1× 35 0.3× 79 3.1k
Puneet Singla United States 21 45 0.2× 118 0.6× 164 1.1× 639 4.5× 461 3.3× 173 2.0k

Countries citing papers authored by Hee‐Seok Oh

Since Specialization
Citations

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

Fields of papers citing papers by Hee‐Seok Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hee‐Seok Oh. 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 Hee‐Seok Oh. The network helps show where Hee‐Seok Oh may publish in the future.

Co-authorship network of co-authors of Hee‐Seok Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Hee‐Seok Oh. A scholar is included among the top collaborators of Hee‐Seok Oh 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 Hee‐Seok Oh. Hee‐Seok Oh 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.
Oh, Hee‐Seok, et al.. (2025). Prediction of Wafer Performance: Use of Functional Outlier Detection and Regression. IEEE Access. 13. 35298–35308. 1 indexed citations
2.
Oh, Hee‐Seok, et al.. (2025). Exploring multiscale methods: reviews and insights. Journal of the Korean Statistical Society. 54(4). 1323–1360.
3.
Oh, Hee‐Seok, et al.. (2025). Cross-Spectral Analysis of Bivariate Graph Signals. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(12). 11141–11151.
4.
Oh, Hee‐Seok, et al.. (2024). Novel sampling method for the von Mises–Fisher distribution. Statistics and Computing. 34(3).
5.
Oh, Hee‐Seok, et al.. (2023). Elastic-band transform for visualization and detection. Pattern Recognition Letters. 166. 119–125. 3 indexed citations
6.
Oh, Hee‐Seok, et al.. (2023). Network time series forecasting using spectral graph wavelet transform. International Journal of Forecasting. 40(3). 971–984. 2 indexed citations
7.
Oh, Hee‐Seok, et al.. (2023). Forecasting South Korea’s presidential election via multiparty dynamic Bayesian modeling. International Journal of Forecasting. 40(1). 124–141. 2 indexed citations
8.
Oh, Hee‐Seok, et al.. (2023). Multi-feature clustering of step data using multivariate functional principal component analysis. Statistical Papers. 65(4). 2109–2134.
9.
Kim, Minji, Hee‐Seok Oh, & Yaeji Lim. (2023). Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform. Journal of Classification. 40(2). 407–431. 1 indexed citations
10.
Oh, Hee‐Seok, et al.. (2020). Pseudo-quantile functional data clustering. Journal of Multivariate Analysis. 178. 104626–104626. 7 indexed citations
11.
Shin, Donghyun, Jin Woo Lee, Jong‐Eun Park, et al.. (2015). Multiple Genes Related to Muscle Identified through a Joint Analysis of a Two-stage Genome-wide Association Study for Racing Performance of 1,156 Thoroughbreds. Asian-Australasian Journal of Animal Sciences. 28(6). 771–781. 13 indexed citations
12.
Oh, Hee‐Seok, et al.. (2015). Variational Mode Decomposition with Missing Data. Korean Journal of Applied Statistics. 28(2). 159–174. 2 indexed citations
13.
Park, Min-Su, et al.. (2014). Genomic Selection for Adjacent Genetic Markers of Yorkshire Pigs Using Regularized Regression Approaches. Asian-Australasian Journal of Animal Sciences. 27(12). 1678–1683. 1 indexed citations
14.
Lim, Yaeji & Hee‐Seok Oh. (2014). Variable selection in quantile regression when the models have autoregressive errors. Journal of the Korean Statistical Society. 43(4). 513–530. 6 indexed citations
15.
Chen, Yunfei & Hee‐Seok Oh. (2014). A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios. IEEE Communications Surveys & Tutorials. 18(1). 848–859. 180 indexed citations
16.
Lee, Jaeyong & Hee‐Seok Oh. (2013). Bayesian regression based on principal components for high-dimensional data. Journal of Multivariate Analysis. 117. 175–192. 7 indexed citations
17.
Lee, Youngjo & Hee‐Seok Oh. (2013). A new sparse variable selection via random-effect model. Journal of Multivariate Analysis. 125. 89–99. 24 indexed citations
18.
Park, Jong‐Eun, Jeongran Lee, Seung‐Yoon Oh, et al.. (2010). Principal components analysis applied to genetic evaluation of racing performance of Thoroughbred race horses in Korea. Livestock Science. 135(2-3). 293–299. 10 indexed citations
19.
Oh, Hee‐Seok, Douglas Nychka, & Thomas C. M. Lee. (2007). The Role of Pseudo Data for Robust Smoothing with Application to Wavelet Regression. Biometrika. 94(4). 893–904. 40 indexed citations
20.
Naveau, Philippe & Hee‐Seok Oh. (2004). Polynomial Wavelet Regression for Images With Irregular Boundaries. IEEE Transactions on Image Processing. 13(6). 773–781. 3 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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