James A. Preiss

906 total citations · 1 hit paper
9 papers, 546 citations indexed

About

James A. Preiss is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Aerospace Engineering. According to data from OpenAlex, James A. Preiss has authored 9 papers receiving a total of 546 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 4 papers in Computer Networks and Communications and 4 papers in Aerospace Engineering. Recurrent topics in James A. Preiss's work include Distributed Control Multi-Agent Systems (4 papers), Robotics and Sensor-Based Localization (4 papers) and Robotic Path Planning Algorithms (4 papers). James A. Preiss is often cited by papers focused on Distributed Control Multi-Agent Systems (4 papers), Robotics and Sensor-Based Localization (4 papers) and Robotic Path Planning Algorithms (4 papers). James A. Preiss collaborates with scholars based in United States and Austria. James A. Preiss's co-authors include Gaurav S. Sukhatme, Nora Ayanian, Wolfgang Hönig, T. K. Satish Kumar, Karol Hausman, Stephan Weiß, Tao Yao, David Millard, Matthew Anderson and Soon‐Jo Chung and has published in prestigious journals such as The International Journal of Robotics Research, IEEE Transactions on Robotics and IEEE Transactions on Automation Science and Engineering.

In The Last Decade

James A. Preiss

9 papers receiving 533 citations

Hit Papers

Crazyswarm: A large nano-quadcopter swarm 2017 2026 2020 2023 2017 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James A. Preiss United States 6 348 265 235 137 97 9 546
Shupeng Lai Singapore 17 397 1.1× 407 1.5× 146 0.6× 178 1.3× 58 0.6× 50 635
Vojtěch Vonásek Czechia 14 399 1.1× 322 1.2× 273 1.2× 207 1.5× 61 0.6× 58 697
Alessandro Renzaglia France 12 226 0.6× 176 0.7× 195 0.8× 77 0.6× 57 0.6× 31 441
Ömer Çetin Türkiye 11 198 0.6× 267 1.0× 142 0.6× 198 1.4× 83 0.9× 22 511
Nikhil Nigam United States 9 256 0.7× 387 1.5× 374 1.6× 165 1.2× 58 0.6× 32 698
Michael Otte United States 12 419 1.2× 262 1.0× 240 1.0× 138 1.0× 129 1.3× 39 653
Alex Kushleyev United States 6 433 1.2× 369 1.4× 261 1.1× 194 1.4× 72 0.7× 6 706
Osman Parlaktuna Türkiye 11 268 0.8× 258 1.0× 155 0.7× 173 1.3× 47 0.5× 44 533
Matthew Turpin United States 9 322 0.9× 252 1.0× 433 1.8× 194 1.4× 62 0.6× 10 608
Michael Kaßecker Germany 5 386 1.1× 466 1.8× 135 0.6× 229 1.7× 82 0.8× 8 749

Countries citing papers authored by James A. Preiss

Since Specialization
Citations

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

Fields of papers citing papers by James A. Preiss

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James A. Preiss

This figure shows the co-authorship network connecting the top 25 collaborators of James A. Preiss. A scholar is included among the top collaborators of James A. Preiss 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 James A. Preiss. James A. Preiss is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Preiss, James A., et al.. (2024). MAGICVFM-Meta-Learning Adaptation for Ground Interaction Control With Visual Foundation Models. IEEE Transactions on Robotics. 41. 180–199. 3 indexed citations
2.
Preiss, James A., et al.. (2023). Resilient Multi-Robot Multi-Target Tracking. IEEE Transactions on Automation Science and Engineering. 21(3). 4311–4327. 4 indexed citations
3.
Preiss, James A., David Millard, Tao Yao, & Gaurav S. Sukhatme. (2022). Tracking Fast Trajectories with a Deformable Object using a Learned Model. 2022 International Conference on Robotics and Automation (ICRA). 1351–1357. 5 indexed citations
4.
Preiss, James A., et al.. (2019). Estimating Metric Scale Visual Odometry from Videos using 3D Convolutional Networks. 5. 265–272. 6 indexed citations
5.
Hönig, Wolfgang, James A. Preiss, T. K. Satish Kumar, Gaurav S. Sukhatme, & Nora Ayanian. (2018). Trajectory Planning for Quadrotor Swarms. IEEE Transactions on Robotics. 34(4). 856–869. 193 indexed citations
6.
Preiss, James A., Karol Hausman, Gaurav S. Sukhatme, & Stephan Weiß. (2018). Simultaneous self-calibration and navigation using trajectory optimization. The International Journal of Robotics Research. 37(13-14). 1573–1594. 16 indexed citations
7.
Preiss, James A., Wolfgang Hönig, Gaurav S. Sukhatme, & Nora Ayanian. (2017). Crazyswarm: A large nano-quadcopter swarm. 3299–3304. 254 indexed citations breakdown →
8.
Preiss, James A., Karol Hausman, Gaurav S. Sukhatme, & Stephan Weiß. (2017). Trajectory Optimization for Self-Calibration and Navigation. 18 indexed citations
9.
Preiss, James A., Wolfgang Hönig, Nora Ayanian, & Gaurav S. Sukhatme. (2017). Downwash-aware trajectory planning for large quadrotor teams. 250–257. 47 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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