Ying Hung

1.2k total citations
37 papers, 818 citations indexed

About

Ying Hung is a scholar working on Computational Theory and Mathematics, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, Ying Hung has authored 37 papers receiving a total of 818 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computational Theory and Mathematics, 19 papers in Management Science and Operations Research and 12 papers in Artificial Intelligence. Recurrent topics in Ying Hung's work include Advanced Multi-Objective Optimization Algorithms (21 papers), Optimal Experimental Design Methods (18 papers) and Probabilistic and Robust Engineering Design (9 papers). Ying Hung is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (21 papers), Optimal Experimental Design Methods (18 papers) and Probabilistic and Robust Engineering Design (9 papers). Ying Hung collaborates with scholars based in United States, Taiwan and Netherlands. Ying Hung's co-authors include V. Roshan Joseph, Agus Sudjianto, Shreyes N. Melkote, Weichung Wang, Ray‐Bing Chen, Chung-Ching Lin, Longcheen Huwang, Rogério Feris, Xinwei Deng and Cheng Zhu and has published in prestigious journals such as Journal of the American Statistical Association, Bioinformatics and Technometrics.

In The Last Decade

Ying Hung

32 papers receiving 780 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ying Hung United States	13	447	316	272	156	95	37	818
Yongdao Zhou China	15	377 0.8×	447 1.4×	168 0.6×	72 0.5×	77 0.8×	52	688
Zan Yang China	19	534 1.2×	206 0.7×	445 1.6×	334 2.1×	122 1.3×	112	1.2k
Emmanuel Vázquez France	11	530 1.2×	307 1.0×	366 1.3×	279 1.8×	29 0.3×	24	848
Michael J. Sasena United States	10	593 1.3×	307 1.0×	273 1.0×	198 1.3×	117 1.2×	12	937
Shan Ba United States	9	261 0.6×	200 0.6×	168 0.6×	108 0.7×	59 0.6×	15	524
Nathalie Bartoli France	16	544 1.2×	200 0.6×	386 1.4×	135 0.9×	71 0.7×	69	1.1k
Mathieu Balesdent France	16	344 0.8×	131 0.4×	442 1.6×	106 0.7×	19 0.2×	46	824
Vladimir Balabanov United States	13	739 1.7×	330 1.0×	530 1.9×	109 0.7×	83 0.9×	37	1.3k
D. Huang United States	3	517 1.2×	307 1.0×	257 0.9×	216 1.4×	19 0.2×	3	734
Claus Hillermeier Germany	6	380 0.9×	90 0.3×	87 0.3×	242 1.6×	66 0.7×	21	666

Countries citing papers authored by Ying Hung

Since Specialization

Citations

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

Fields of papers citing papers by Ying Hung

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ying Hung

This figure shows the co-authorship network connecting the top 25 collaborators of Ying Hung. A scholar is included among the top collaborators of Ying Hung 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 Ying Hung. Ying Hung 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

Chen, Ray‐Bing, et al.. (2023). Indicator-based Bayesian variable selection for Gaussian process models in computer experiments. Computational Statistics & Data Analysis. 185. 107757–107757.

Hung, Ying, et al.. (2023). Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19. Journal of the Royal Statistical Society Series C (Applied Statistics). 73(1). 47–64. 3 indexed citations

Wang, Wenjia, et al.. (2023). Functional-Input Gaussian Processes with Applications to Inverse Scattering Problems. Statistica Sinica. 1 indexed citations

Gameiro, Marcio, et al.. (2023). Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees. 3065–3072. 1 indexed citations

Sengul, Mert Y., Nadire Nayir, Yawei Gao, et al.. (2021). INDEEDopt: a deep learning-based ReaxFF parameterization framework. npj Computational Materials. 7(1). 28 indexed citations

Hung, Ying, et al.. (2021). Varying coefficient frailty models with applications in single molecular experiments. Biometrics. 78(2). 474–486.

Hung, Ying, et al.. (2021). Optimal Simulator Selection. Journal of the American Statistical Association. 118(542). 1264–1271.

Hung, Ying, et al.. (2020). Gaussian Process Prediction using Design-Based Subsampling. Statistica Sinica. 3 indexed citations

Hung, Ying, et al.. (2019). A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series. Journal of the American Statistical Association. 115(530). 945–956. 11 indexed citations

10.

Hung, Ying, et al.. (2017). Variable Selection for Gaussian Process Models using Experimental Design-Based Subagging. Statistica Sinica. 6 indexed citations

11.

Deng, Xinwei, et al.. (2014). Design for computer experiments with qualitative and quantitative factors. Statistica Sinica. 26 indexed citations

12.

Hung, Ying & V. Roshan Joseph. (2014). Discussion of “Three-phase optimal design of sensitivity experiments” by Wu and Tian. Journal of Statistical Planning and Inference. 149. 16–19. 2 indexed citations

13.

Hung, Ying, V. Roshan Joseph, & Shreyes N. Melkote. (2013). Analysis of Computer Experiments With Functional Response. Technometrics. 57(1). 35–44. 41 indexed citations

14.

Chen, Ray‐Bing, et al.. (2013). Discrete particle swarm optimization for constructing uniform design on irregular regions. Computational Statistics & Data Analysis. 72. 282–297. 21 indexed citations

15.

Chen, Ray‐Bing, et al.. (2012). Optimizing Latin hypercube designs by particle swarm. Statistics and Computing. 23(5). 663–676. 61 indexed citations

16.

Hung, Ying. (2011). Penalized blind kriging in computer experiments. Statistica Sinica. 21(3). 1171–1190. 31 indexed citations

17.

Ume, I. Charles, et al.. (2010). Effects of Warpage on Fatigue Reliability of Solder Bumps: Experimental and Analytical Studies. IEEE Transactions on Advanced Packaging. 33(2). 314–322. 4 indexed citations

18.

Joseph, V. Roshan & Ying Hung. (2008). ORTHOGONAL-MAXIMIN LATIN HYPERCUBE DESIGNS. Statistica Sinica. 18(1). 171–186. 198 indexed citations

19.

Joseph, V. Roshan, Ying Hung, & Agus Sudjianto. (2008). Blind Kriging: A New Method for Developing Metamodels. Journal of Mechanical Design. 130(3). 204 indexed citations

20.

Huwang, Longcheen & Ying Hung. (2007). EFFECT OF MEASUREMENT ERROR ON MONITORING MULTIVARIATE PROCESS VARIABILITY. Statistica Sinica. 17(2). 749–760. 19 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.

Explore authors with similar magnitude of impact