Scott A. Starks

58 papers receiving 287 citations

Peers

Scott A. Starks
Comparison fields: 5 of 81
  • Statistics, Probability and Uncertainty 82
  • Computational Theory and Mathematics 94
  • Statistics and Probability 35
  • Architecture 6
  • Management Science and Operations Research 39
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Rajan Srinivasan Netherlands
Niklas Lind United Kingdom
Rui Tuo United States
J.L. Maryak United States
Bernard Hanzon Netherlands
Yoshifusa Ito Japan
Kei Takeuchi Japan
Eva Riccomagno Italy
J.L. Hammond United States
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Citations per year

Countries citing papers authored by Scott A. Starks

Since Specialization
Citations

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

Fields of papers citing papers by Scott A. Starks

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Scott A. Starks. 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 Scott A. Starks. The network helps show where Scott A. Starks may publish in the future.

Co-authors

The 25 scholars most cited alongside Scott A. Starks, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Scott A. Starks Line = papers co-authored together Scott A. Starks links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 70 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200638
2 200634
3 200427
4 197725
5 200617
6 200616
7 200315
8
Towards Combining Probabilistic and Interval Uncertainty in Engineering Calculations
200413
9 20049
10 20048
11 20007
12 20026
13
Multi-Resolution Data Processing: It is Necessary, it is Possible, it is Fundamental
19975
14
An Optimal FFT-Based Algorithm for Mosaicking Images, with Applications to Satellite Imaging and Web Search
19995
15 20135
16 20085
17 20045
18 20075
19 20054
20 20024

About Scott A. Starks

Scott A. Starks is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Aerospace Engineering, Signal Processing and Control and Systems Engineering, having authored 70 papers that have together received 322 indexed citations. Recurring topics across this work include Numerical Methods and Algorithms (13 papers), Engineering Education and Pedagogy (7 papers), Probabilistic and Robust Engineering Design (6 papers), Spacecraft Design and Technology (5 papers), Logic, Reasoning, and Knowledge (5 papers), Multi-Criteria Decision Making (5 papers), Data Management and Algorithms (4 papers) and Engineering Education and Curriculum Development (4 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (82 citations), Computational Theory and Mathematics (94 citations), Statistics and Probability (35 citations), Architecture (6 citations) and Management Science and Operations Research (39 citations). Scott A. Starks has collaborated with scholars based in United States, Canada and Russia. Frequent co-authors include Владик Крейнович, Luc Longpré, Jan Beck, G. Randy Keller, Nedialko S. Nedialkov, Gang Xiang, Roberto Torres, Mary Jo Spencer, Hung T. Nguyen and James S. Mattson. Their work appears in journals such as Journal of Computational and Applied Mathematics, IEEE Computer Applications in Power, International Journal of Approximate Reasoning, Analytical Chemistry and Geocarto International.

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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