Ikjin Lee

4.8k total citations · 1 hit paper
158 papers, 3.7k citations indexed

About

Ikjin Lee is a scholar working on Statistics, Probability and Uncertainty, Computational Theory and Mathematics and Civil and Structural Engineering. According to data from OpenAlex, Ikjin Lee has authored 158 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 97 papers in Statistics, Probability and Uncertainty, 85 papers in Computational Theory and Mathematics and 44 papers in Civil and Structural Engineering. Recurrent topics in Ikjin Lee's work include Probabilistic and Robust Engineering Design (97 papers), Advanced Multi-Objective Optimization Algorithms (85 papers) and Optimal Experimental Design Methods (37 papers). Ikjin Lee is often cited by papers focused on Probabilistic and Robust Engineering Design (97 papers), Advanced Multi-Objective Optimization Algorithms (85 papers) and Optimal Experimental Design Methods (37 papers). Ikjin Lee collaborates with scholars based in South Korea, United States and China. Ikjin Lee's co-authors include Kyung K. Choi, Liang Zhao, Yongsu Jung, David Gorsich, Yoojeong Noh, Namwoo Kang, Liu Du, Sangeun Oh, Hyunkyoo Cho and Bong Jae Lee and has published in prestigious journals such as Journal of Cleaner Production, Scientific Reports and Nano Energy.

In The Last Decade

Ikjin Lee

150 papers receiving 3.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Generative Design: Integration of Topology Optimization and Generative Models

2019 292 citations Yongsu Jung, Ikjin Lee et al. Journal of Mechanical Design profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ikjin Lee South Korea	32	2.0k	1.5k	1.2k	591	579	158	3.7k
Zhen Hu United States	35	2.1k 1.1×	1.0k 0.7×	1.1k 0.9×	493 0.8×	647 1.1×	228	4.6k
Zhenzhou Lü China	34	3.4k 1.7×	1.1k 0.8×	1.8k 1.4×	546 0.9×	1.1k 2.0×	198	4.5k
Pingfeng Wang United States	33	1.8k 0.9×	676 0.5×	1.1k 0.9×	380 0.6×	581 1.0×	222	4.8k
Xiaojun Wang China	34	2.5k 1.3×	981 0.7×	2.4k 1.9×	150 0.3×	925 1.6×	212	4.1k
Nam Ho Kim United States	38	1.2k 0.6×	740 0.5×	1.3k 1.0×	289 0.5×	1.4k 2.4×	272	5.4k
Ruichen Jin United States	11	1.3k 0.6×	1.6k 1.1×	557 0.4×	802 1.4×	297 0.5×	20	2.8k
G. Gary Wang Canada	17	1.0k 0.5×	1.6k 1.1×	487 0.4×	677 1.1×	202 0.3×	31	3.1k
Patrick Koch United States	19	1.0k 0.5×	1.4k 1.0×	452 0.4×	680 1.2×	170 0.3×	72	3.2k
Gyung-Jin Park South Korea	24	759 0.4×	779 0.5×	1.1k 0.9×	279 0.5×	512 0.9×	144	2.4k
Ramana V. Grandhi United States	42	2.7k 1.4×	2.1k 1.5×	3.6k 2.9×	411 0.7×	2.6k 4.5×	283	7.2k

Countries citing papers authored by Ikjin Lee

Since Specialization

Citations

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

Fields of papers citing papers by Ikjin Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ikjin Lee

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zhang, Tongzhou, et al.. (2025). Long-term extreme response evaluation of stochastic models using adaptive stochastic importance sampling. Reliability Engineering & System Safety. 260. 111028–111028.

Hu, Weifei, et al.. (2025). Spatiotemporal-dependent reliability analysis with adaptive sampling physics-informed neural networks. Reliability Engineering & System Safety. 265. 111560–111560. 1 indexed citations

Lee, Mingyu, et al.. (2025). A novel low-fidelity-guided design of experiments for multi-fidelity surrogate modeling. Advanced Engineering Informatics. 69. 104076–104076.

Ahmed, Faez, Wei Chen, Christopher McComb, et al.. (2025). Special Issue: Design by Data: Cultivating Datasets for Engineering Design. Journal of Mechanical Design. 147(4). 1 indexed citations

Jung, Minkyo, et al.. (2025). Efficient Optimization of Magnetic Properties of Fe–4.5Si Alloy in Laser Powder Bed Fusion (LPBF). International Journal of Precision Engineering and Manufacturing-Green Technology. 12(4). 1133–1146. 3 indexed citations

Jung, Yongsu, et al.. (2024). Confidence-based design optimization using multivariate kernel density estimation under insufficient input data. Probabilistic Engineering Mechanics. 78. 103702–103702. 6 indexed citations

Lee, Mingyu, et al.. (2024). Optimization framework for surveillance camera layouts considering infiltration routes in general outposts (GOPs). Expert Systems with Applications. 263. 125804–125804.

Lee, Mingyu, et al.. (2024). An efficient multi-fidelity design optimization framework for a thermoelectric generator system. Energy Conversion and Management. 315. 118788–118788. 6 indexed citations

Lee, Ikjin, et al.. (2024). An automated framework for material property calibration in loudspeaker simulation model. Advances in Engineering Software. 197. 103748–103748. 1 indexed citations

10.

Gu, Jimin, Junseong Ahn, Jungrak Choi, et al.. (2024). Auxetic kirigami structure-based self-powered strain sensor with customizable performance using machine learning. Nano Energy. 130. 110124–110124. 16 indexed citations

11.

Wang, Xuan, et al.. (2024). Surrogate model-based method for reliability-oriented buckling topology optimization under random field load uncertainty. Structures. 63. 106382–106382. 4 indexed citations

12.

Hu, Weifei, et al.. (2024). Reliability-based design optimization: a state-of-the-art review of its methodologies, applications, and challenges. Structural and Multidisciplinary Optimization. 67(9). 11 indexed citations

13.

Lee, Juyoung, Mingyu Lee, Bong Jae Lee, & Ikjin Lee. (2024). A comprehensive multi-fidelity surrogate framework based on Gaussian process for datasets with heterogeneous responses. Knowledge-Based Systems. 295. 111827–111827. 6 indexed citations

14.

Lee, Junhyeong, Donggeun Park, Mingyu Lee, et al.. (2023). Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review. Materials Horizons. 10(12). 5436–5456. 59 indexed citations

15.

Li, Kunpeng, Qingye Li, Liye Lv, et al.. (2023). A nonlinearity integrated bi-fidelity surrogate model based on nonlinear mapping. Structural and Multidisciplinary Optimization. 66(9). 6 indexed citations

16.

Seo, Junyong, Pil-Hoon Jung, Mingeon Kim, et al.. (2019). Design of a Broadband Solar Thermal Absorber Using a Deep Neural Network and Experimental Demonstration of Its Performance. Scientific Reports. 9(1). 15028–15028. 22 indexed citations

17.

Qin, Caiyan, et al.. (2019). Modified screening-based Kriging method with cross validation and application to engineering design. Applied Mathematical Modelling. 70. 626–642. 38 indexed citations

18.

Lee, Ikjin, et al.. (2017). Bayesian Network and Feature Selection for Rank Deficient Inverse Problem. 4(11). 1 indexed citations

19.

Lamb, David, David Gorsich, Kyung K. Choi, Yoojeong Noh, & Ikjin Lee. (2008). The Use of Copulas and MPP-Based Dimension Reduction Method (DRM) to Assess and Mitigate Engineering Risk in the Army Ground Vehicle Fleet. Defense Technical Information Center (DTIC).

20.

Lee, Ikjin, et al.. (2007). Dimension reduction method for reliability-based robust design optimization. Computers & Structures. 86(13-14). 1550–1562. 148 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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