Jonathan DeCastro

44 papers receiving 545 citations

Peers

Jonathan DeCastro
Comparison fields: 5 of 54
  • Software 48
  • Automotive Engineering 132
  • Control and Systems Engineering 215
  • Computer Vision and Pattern Recognition 154
  • Safety, Risk, Reliability and Quality 60
Replace Michael Hofbaur with:
Michael Hofbaur Austria
Jaime F. Fisac United States
Jeremy Gillula United States
Nicola Bezzo United States
Emília Villani Brazil
Markus Koschi Germany
John Kaneshige United States
Melissa Greeff Canada
Kim P. Wabersich Switzerland
Gennaro Notomista United States
Jonathan DeCastro relative to Michael Hofbaur Austria Michael Hofbaur's profile →
Citations per field
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Citations per year

Countries citing papers authored by Jonathan DeCastro

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan DeCastro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jonathan DeCastro, 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 Jonathan DeCastro Line = papers co-authored together Jonathan DeCastro links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201256
2 202054
3 201751
4 202142
5 201441
6 200741
7 201727
8 201524
9 201024
10 201120
11 201315
12 201012
13 201012
14 201611
15 20159
16 20199
17 20209
18 20208
19 20097
20 20216

About Jonathan DeCastro

Jonathan DeCastro is a scholar working on Automotive Engineering, Computational Theory and Mathematics, Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 46 papers that have together received 552 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (16 papers), Formal Methods in Verification (13 papers), Robotic Path Planning Algorithms (8 papers), Traffic and Road Safety (6 papers), Fault Detection and Control Systems (5 papers), Human-Automation Interaction and Safety (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Model-Driven Software Engineering Techniques (4 papers). The work is most often cited by research in Software (48 citations), Automotive Engineering (132 citations), Control and Systems Engineering (215 citations), Computer Vision and Pattern Recognition (154 citations) and Safety, Risk, Reliability and Quality (60 citations). Jonathan DeCastro has collaborated with scholars based in United States, Switzerland and Canada. Frequent co-authors include Hadas Kress‐Gazit, Liang Tang, Daniela Rus, Xiaodong Zhang, Kai Goebel, Vasumathi Raman, Guy Rosman, Javier Alonso–Mora, Liang Tang and Bin Zhang. Their work appears in journals such as IEEE Robotics and Automation Letters, The International Journal of Robotics Research, IEEE Transactions on Control Systems Technology, Autonomous Robots and IEEE Transactions on Industrial Electronics.

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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