John J. Borkowski

1.8k total citations
56 papers, 1.3k citations indexed

About

John J. Borkowski is a scholar working on Management Science and Operations Research, Computational Theory and Mathematics and Statistics, Probability and Uncertainty. According to data from OpenAlex, John J. Borkowski has authored 56 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Management Science and Operations Research, 26 papers in Computational Theory and Mathematics and 15 papers in Statistics, Probability and Uncertainty. Recurrent topics in John J. Borkowski's work include Optimal Experimental Design Methods (36 papers), Advanced Multi-Objective Optimization Algorithms (26 papers) and Manufacturing Process and Optimization (11 papers). John J. Borkowski is often cited by papers focused on Optimal Experimental Design Methods (36 papers), Advanced Multi-Objective Optimization Algorithms (26 papers) and Manufacturing Process and Optimization (11 papers). John J. Borkowski collaborates with scholars based in United States, Thailand and Japan. John J. Borkowski's co-authors include Friedrich Pukelsheim, James M. Lucas, David J. Varricchio, John R. Horner, Frankie D. Jackson, Roger L. Sheley, Monica L. Pokorny, Catherine A. Zabinski, Richard E. Engel and Tony J. Svejcar and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

John J. Borkowski

51 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John J. Borkowski United States 20 409 311 299 273 184 56 1.3k
P.D. Roberts United Kingdom 24 30 0.1× 124 0.4× 222 0.7× 132 0.5× 61 0.3× 147 2.0k
Daniel G. Brooks United States 8 59 0.1× 20 0.1× 136 0.5× 86 0.3× 31 0.2× 17 766
Donald R. Porter United States 9 45 0.1× 38 0.1× 118 0.4× 70 0.3× 51 0.3× 13 900
László Szeidl Hungary 13 21 0.1× 43 0.1× 122 0.4× 168 0.6× 20 0.1× 40 1.0k
Jan Engel Netherlands 14 167 0.4× 68 0.2× 45 0.2× 43 0.2× 111 0.6× 37 638
Hervé Cardot France 23 104 0.3× 47 0.2× 141 0.5× 39 0.1× 110 0.6× 65 2.0k
Richard D. De Veaux United States 13 58 0.1× 42 0.1× 52 0.2× 50 0.2× 45 0.2× 37 936
Régis Sabbadin France 17 242 0.6× 74 0.2× 124 0.4× 137 0.5× 6 0.0× 49 897
J. T. Gene Hwang United States 19 288 0.7× 38 0.1× 71 0.2× 32 0.1× 78 0.4× 49 1.8k
Jorge Orestes Cerdeira Portugal 20 15 0.0× 70 0.2× 474 1.6× 428 1.6× 4 0.0× 50 1.3k

Countries citing papers authored by John J. Borkowski

Since Specialization
Citations

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

Fields of papers citing papers by John J. Borkowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John J. Borkowski. 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 John J. Borkowski. The network helps show where John J. Borkowski may publish in the future.

Co-authorship network of co-authors of John J. Borkowski

This figure shows the co-authorship network connecting the top 25 collaborators of John J. Borkowski. A scholar is included among the top collaborators of John J. Borkowski 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 John J. Borkowski. John J. Borkowski 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.
Borkowski, John J., et al.. (2025). Robust D-Optimal Mixture Designs Under Manufacturing Tolerances via Multi-Objective NSGA-II. Mathematics. 13(18). 2950–2950.
2.
3.
Walsh, Stephen J., T. Bolton, & John J. Borkowski. (2024). Generating optimal designs with user‐specified pure replication structure. Quality and Reliability Engineering International. 40(4). 2019–2047. 1 indexed citations
4.
Borkowski, John J., et al.. (2023). Eco-friendly magnetic biochar from Leb Mu Nang banana peel: Response surface methodology optimization for Cd(II) adsorption from synthetic wastewater. Bioresource Technology Reports. 25. 101743–101743. 9 indexed citations
5.
Walsh, Stephen J. & John J. Borkowski. (2022). Improved G-Optimal Designs for Small Exact Response Surface Scenarios: Fast and Efficient Generation via Particle Swarm Optimization. Mathematics. 10(22). 4245–4245. 4 indexed citations
6.
Walsh, Stephen J. & John J. Borkowski. (2022). Generating exact optimal designs via particle swarm optimization: Assessing efficacy and efficiency via case study. Quality Engineering. 35(2). 304–323. 5 indexed citations
7.
Borkowski, John J., et al.. (2021). Using geometric mean to compute robust mixture designs. Quality and Reliability Engineering International. 37(8). 3441–3464. 2 indexed citations
8.
Johnson, Kara, Jennifer L. Walsh, Yuri A. Amirkhanian, John J. Borkowski, & Nicole Bohme Carnegie. (2021). Using a novel genetic algorithm to assess peer influence on willingness to use pre-exposure prophylaxis in networks of Black men who have sex with men. Applied Network Science. 6(1). 3 indexed citations
9.
Borkowski, John J., et al.. (2018). The Construction of a Model-Robust IV-Optimal Mixture Designs Using a Genetic Algorithm. Mathematical and Computational Applications. 23(2). 25–25. 5 indexed citations
10.
Borkowski, John J., et al.. (2017). Using Genetic Algorithms to Generate Dw and Gw-Optimal Response Surface Designs in the Hypercube. 15(2). 157–166. 1 indexed citations
11.
Borkowski, John J., et al.. (2017). Stratified Adaptive Cluster Sampling with Spatially Clustered Secondary Units. 15(2). 111–127. 1 indexed citations
12.
Borkowski, John J., et al.. (2014). Using a genetic algorithm to generate D s -optimal designs for mixture experiments in a simplex region. Lobachevskii Journal of Mathematics. 35(2). 122–137. 3 indexed citations
13.
Borkowski, John J., et al.. (2014). Using a genetic algorithm to generate Ds-optimal designs with bounded D-efficiencies for mixture experiments. 12(2). 191–205. 2 indexed citations
14.
Borkowski, John J., et al.. (2012). Expected Mean Squares for the Random Effects One-Way ANOVA Model when Sampling from a Finite Population. 10(1). 121–128. 1 indexed citations
15.
Bergman, Eric J., et al.. (2006). Assessment Of Prey Vulnerability Through Analysis Of Wolf Movements And Kill Sites. Ecological Applications. 16(1). 273–284. 105 indexed citations
16.
Borkowski, John J., et al.. (2006). BEHAVIORAL RESPONSES OF BISON AND ELK IN YELLOWSTONE TO SNOWMOBILES AND SNOW COACHES. Ecological Applications. 16(5). 1911–1925. 36 indexed citations
17.
Lucas, James M., et al.. (2004). Factorial Experiments When Factor Levels are Not Necessarily Reset. Journal of Quality Technology. 36(1). 1–11. 35 indexed citations
18.
Lucas, James M., et al.. (2004). Factorial experiments when factor levels are not necessarily reset. Quality Engineering. 49(5). 561–564. 4 indexed citations
19.
Borkowski, John J.. (2003). Simple Latin Square Sampling ± k Designs. Communication in Statistics- Theory and Methods. 32(1). 215–237. 3 indexed citations
20.
Borkowski, John J. & James M. Lucas. (1997). Designs of Mixed Resolution for Process Robustness Studies. Technometrics. 39(1). 63–70. 33 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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