Scott A. Starks

70 papers receiving 403 citations

Peers

Scott A. Starks
Comparison fields: 5 of 87
  • Statistics, Probability and Uncertainty 97
  • Computational Theory and Mathematics 154
  • Statistics and Probability 49
  • Artificial Intelligence 157
  • Management Science and Operations Research 58
Replace Rajan Srinivasan with:
Rajan Srinivasan Netherlands
Javier Yáñez Spain
J.L. Maryak United States
Matthew Plumlee United States
Leonid G. Khachiyan United States
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Niklas Lind Switzerland
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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 78 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200647
2 200643
3 200432
4 200629
5 197726
6 200320
7 200618
8
Towards Combining Probabilistic and Interval Uncertainty in Engineering Calculations
200416
9 200413
10 200111
11 200411
12 200310
13 20069
14 20049
15 20088
16 20027
17 20007
18 19976
19
Multi-Resolution Data Processing: It is Necessary, it is Possible, it is Fundamental
19975
20
An Optimal FFT-Based Algorithm for Mosaicking Images, with Applications to Satellite Imaging and Web Search
19995

About Scott A. Starks

Scott A. Starks is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Aerospace Engineering, Control and Systems Engineering and Signal Processing, having authored 78 papers that have together received 446 indexed citations. Recurring topics across this work include Numerical Methods and Algorithms (15 papers), Engineering Education and Pedagogy (7 papers), Probabilistic and Robust Engineering Design (7 papers), Spacecraft Design and Technology (5 papers), Data Management and Algorithms (5 papers), Neural Networks and Applications (5 papers), Multi-Criteria Decision Making (5 papers) and Logic, Reasoning, and Knowledge (5 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (97 citations), Computational Theory and Mathematics (154 citations), Statistics and Probability (49 citations), Artificial Intelligence (157 citations) and Management Science and Operations Research (58 citations). Scott A. Starks has collaborated with scholars based in United States, Russia and Canada. Frequent co-authors include Владик Крейнович, Luc Longpré, Jan Beck, Gang Xiang, G. Randy Keller, Nedialko S. Nedialkov, Scott Ferson, Roberto Torres, Hung T. Nguyen and Mary Jo Spencer. Their work appears in journals such as IEEE Computer Applications in Power, Journal of Computational and Applied Mathematics, Geocarto International, IEEE Transactions on Aerospace and Electronic Systems and Computers & Electrical Engineering.

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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