Kyle V. Camarda

1.5k total citations · 1 hit paper
34 papers, 994 citations indexed

About

Kyle V. Camarda is a scholar working on Control and Systems Engineering, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Kyle V. Camarda has authored 34 papers receiving a total of 994 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Control and Systems Engineering, 8 papers in Molecular Biology and 8 papers in Computational Theory and Mathematics. Recurrent topics in Kyle V. Camarda's work include Computational Drug Discovery Methods (7 papers), Process Optimization and Integration (7 papers) and Machine Learning in Materials Science (4 papers). Kyle V. Camarda is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Process Optimization and Integration (7 papers) and Machine Learning in Materials Science (4 papers). Kyle V. Camarda collaborates with scholars based in United States, Netherlands and Denmark. Kyle V. Camarda's co-authors include Paulette Spencer, Elizabeth M. Topp, John Eslick, Qiang Ye, Costas D. Maranas, J. Lawrence Katz, Brenda Bohaty, Orestes Marangos, Anil Misra and Yong Wang and has published in prestigious journals such as Journal of Biomechanics, Industrial & Engineering Chemistry Research and Chemical Engineering Science.

In The Last Decade

Kyle V. Camarda

32 papers receiving 973 citations

Hit Papers

Adhesive/Dentin Interface: The Weak Link in the Composite... 2010 2026 2015 2020 2010 100 200 300
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.

  1. Adhesive/Dentin Interface: The Weak Link in the Composite Restoration
    2010 360 citations Paulette Spencer, Qiang Ye et al. Annals of Biomedical Engineering profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kyle V. Camarda United States 15 380 191 181 144 139 34 994
Wei Shen China 16 40 0.1× 121 0.6× 62 0.3× 12 0.1× 17 0.1× 56 798
Carlos Fernandes Portugal 22 17 0.0× 113 0.6× 10 0.1× 7 0.0× 55 0.4× 58 1.2k
Prı́amo A. Melo Brazil 16 9 0.0× 99 0.5× 8 0.0× 9 0.1× 20 0.1× 79 744
Yuehong Wang China 15 19 0.1× 6 0.0× 62 0.3× 28 0.2× 4 0.0× 56 817
Takahiro Kawai Japan 16 59 0.2× 4 0.0× 96 0.5× 19 0.1× 4 0.0× 67 773
Yong-Jin Lee South Korea 15 22 0.1× 17 0.1× 27 0.1× 3 0.0× 12 0.1× 39 746
Toshihiro Takamatsu Japan 17 12 0.0× 575 3.0× 12 0.1× 18 0.1× 5 0.0× 64 1.2k
Shengmiao Zhang China 24 28 0.1× 3 0.0× 26 0.1× 11 0.1× 10 0.1× 94 1.7k
Meiyi Liu China 13 3 0.0× 53 0.3× 6 0.0× 74 0.5× 12 0.1× 48 1.1k

Countries citing papers authored by Kyle V. Camarda

Since Specialization
Citations

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

Fields of papers citing papers by Kyle V. Camarda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kyle V. Camarda. 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 Kyle V. Camarda. The network helps show where Kyle V. Camarda may publish in the future.

Co-authorship network of co-authors of Kyle V. Camarda

This figure shows the co-authorship network connecting the top 25 collaborators of Kyle V. Camarda. A scholar is included among the top collaborators of Kyle V. Camarda 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 Kyle V. Camarda. Kyle V. Camarda 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.
Zondervan, Edwin, Cristhian Almeida-Rivera, & Kyle V. Camarda. (2023). Product-Driven Process Design.
2.
Camarda, Kyle V., et al.. (2022). Using Artificial Neural Networks to Predict Physical Properties of Membrane Polymers. Chemie Ingenieur Technik. 95(3). 363–367. 2 indexed citations
3.
Camarda, Kyle V., et al.. (2021). Shaping the future energy markets with hybrid multimicrogrids by sequential least squares programming. Physical Sciences Reviews. 8(1). 121–156. 5 indexed citations
4.
Boone, Kyle, et al.. (2021). Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides. BMC Bioinformatics. 22(1). 239–239. 55 indexed citations
5.
Zondervan, Edwin, Cristhian Almeida-Rivera, & Kyle V. Camarda. (2020). Product-Driven Process Design.
6.
Boone, Kyle, Kyle V. Camarda, Paulette Spencer, & Candan Tamerler. (2018). Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries. BMC Bioinformatics. 19(1). 469–469. 28 indexed citations
7.
8.
Ye, Qiang, et al.. (2016). Computer-aided molecular design of water compatible visible light photosensitizers for dental adhesive. Chemical Engineering Science. 159. 131–139. 9 indexed citations
9.
Tula, Anjan K., et al.. (2016). Sustainable process design & analysis of hybrid separations. Computers & Chemical Engineering. 105. 96–104. 25 indexed citations
10.
11.
Topp, Elizabeth M., et al.. (2013). Protein aggregation and lyophilization: Protein structural descriptors as predictors of aggregation propensity. Computers & Chemical Engineering. 58(2013). 369–377. 13 indexed citations
12.
Topp, Elizabeth M., et al.. (2011). Use of glass transitions in carbohydrate excipient design for lyophilized protein formulations. Computers & Chemical Engineering. 36(10). 208–216. 12 indexed citations
13.
Spencer, Paulette, Qiang Ye, Jong-Gu Park, et al.. (2010). Adhesive/Dentin Interface: The Weak Link in the Composite Restoration. Annals of Biomedical Engineering. 38(6). 1989–2003. 360 indexed citations breakdown →
14.
Eslick, John, et al.. (2008). A computational molecular design framework for crosslinked polymer networks. Computers & Chemical Engineering. 33(5). 954–963. 20 indexed citations
15.
16.
Camarda, Kyle V., et al.. (2005). An Optimization Approach to the Design of Value-Added Soybean Oil Products. Industrial & Engineering Chemistry Research. 44(12). 4361–4367. 14 indexed citations
17.
Camarda, Kyle V., et al.. (2003). Pharmaceutical product design using combinatorial optimization. Computers & Chemical Engineering. 28(3). 425–434. 28 indexed citations
18.
Camarda, Kyle V. & Costas D. Maranas. (1999). Optimization in Polymer Design Using Connectivity Indices. Industrial & Engineering Chemistry Research. 38(5). 1884–1892. 71 indexed citations
19.
Camarda, Kyle V., et al.. (1999). Design of surfactant solutions with optimal macroscopic properties. Computers & Chemical Engineering. 23. S467–S470. 6 indexed citations
20.
Camarda, Kyle V. & Mark A. Stadtherr. (1998). Frontal solvers for process engineering: local row ordering strategies. Computers & Chemical Engineering. 22(3). 333–341. 4 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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