Nancy M. Amato

8.8k total citations
221 papers, 5.3k citations indexed

About

Nancy M. Amato is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Control and Systems Engineering. According to data from OpenAlex, Nancy M. Amato has authored 221 papers receiving a total of 5.3k indexed citations (citations by other indexed papers that have themselves been cited), including 126 papers in Computer Vision and Pattern Recognition, 63 papers in Aerospace Engineering and 55 papers in Control and Systems Engineering. Recurrent topics in Nancy M. Amato's work include Robotic Path Planning Algorithms (101 papers), Robotics and Sensor-Based Localization (61 papers) and Computational Geometry and Mesh Generation (34 papers). Nancy M. Amato is often cited by papers focused on Robotic Path Planning Algorithms (101 papers), Robotics and Sensor-Based Localization (61 papers) and Computational Geometry and Mesh Generation (34 papers). Nancy M. Amato collaborates with scholars based in United States, Mexico and Germany. Nancy M. Amato's co-authors include Jyh‐Ming Lien, Guang Song, O. Burçhan Bayazıt, Shawna Thomas, Lucia K. Dale, You Wu, Daniel Vallejo, Christopher A. Jones, Peter F. Stiller and Suman Chakravorty and has published in prestigious journals such as Bioinformatics, Journal of Molecular Biology and Journal of Computational Physics.

In The Last Decade

Nancy M. Amato

214 papers receiving 5.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nancy M. Amato United States 40 3.3k 1.7k 1.5k 956 777 221 5.3k
Sertaç Karaman United States 41 4.4k 1.3× 2.3k 1.3× 2.0k 1.3× 887 0.9× 1.2k 1.5× 173 7.0k
Jean‐Claude Latombe United States 39 8.1k 2.4× 4.2k 2.4× 4.3k 2.9× 1.5k 1.6× 1.6k 2.1× 98 10.9k
J.-C. Latombe United States 32 7.3k 2.2× 3.9k 2.2× 3.6k 2.4× 1.1k 1.1× 1.2k 1.5× 62 8.4k
Călin Belta United States 41 1.9k 0.6× 456 0.3× 2.0k 1.3× 1.2k 1.2× 2.3k 2.9× 262 6.7k
Evangelos Milios Canada 35 2.8k 0.9× 2.6k 1.5× 400 0.3× 1.3k 1.4× 2.2k 2.8× 255 6.7k
P.K. Khosla United States 30 2.3k 0.7× 994 0.6× 1.1k 0.7× 1.1k 1.1× 804 1.0× 100 4.3k
Bernard Chazelle United States 52 4.0k 1.2× 946 0.5× 197 0.1× 1.2k 1.3× 1.4k 1.8× 193 9.9k
Marco Pavone United States 40 1.6k 0.5× 1.0k 0.6× 1.6k 1.1× 1.0k 1.1× 833 1.1× 255 5.6k
Michael S. Branicky United States 30 963 0.3× 762 0.4× 7.9k 5.3× 2.9k 3.1× 681 0.9× 82 10.3k
Debasish Ghose India 42 1.9k 0.6× 4.2k 2.4× 1.6k 1.1× 2.5k 2.6× 934 1.2× 347 7.4k

Countries citing papers authored by Nancy M. Amato

Since Specialization
Citations

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

Fields of papers citing papers by Nancy M. Amato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nancy M. Amato. 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 Nancy M. Amato. The network helps show where Nancy M. Amato may publish in the future.

Co-authorship network of co-authors of Nancy M. Amato

This figure shows the co-authorship network connecting the top 25 collaborators of Nancy M. Amato. A scholar is included among the top collaborators of Nancy M. Amato 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 Nancy M. Amato. Nancy M. Amato 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.
Williams, Tiffani L., et al.. (2024). Work in Progress: A New Graduate Certificate to Broaden Participation in Computing. Papers on Engineering Education Repository (American Society for Engineering Education). 1 indexed citations
3.
Morales, Marco, et al.. (2021). Parallel Hierarchical Composition Conflict-Based Search for Optimal Multi-Agent Pathfinding. IEEE Robotics and Automation Letters. 6(4). 7001–7008. 17 indexed citations
4.
Adams, Michael P., Marvin L. Adams, Lawrence Rauchwerger, et al.. (2020). Provably optimal parallel transport sweeps on semi-structured grids. Journal of Computational Physics. 407. 109234–109234. 19 indexed citations
5.
Thomas, Shawna, et al.. (2016). Adaptive local learning in sampling based motion planning for protein folding. BMC Systems Biology. 10(S2). 49–49. 13 indexed citations
6.
Amato, Nancy M., et al.. (2014). SCCMulti. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 393–394. 2 indexed citations
7.
Jacobs, Sam Adé, et al.. (2014). Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 573–582. 2 indexed citations
8.
Thomas, Shawna, et al.. (2013). Rigidity analysis for protein motion and folding core identification. National Conference on Artificial Intelligence. 38–43. 1 indexed citations
9.
Adams, Michael P., et al.. (2012). Efficient Massively Parallel Transport Sweeps. Transactions of the American Nuclear Society. 107(1). 477–481. 12 indexed citations
10.
Amato, Nancy M., et al.. (2007). Metrics for sampling-based motion planning. 1 indexed citations
11.
Lien, Jyh-Ming, et al.. (2006). An obstacle-based rapidly-exploring random tree. 895–900. 147 indexed citations
12.
Thomas, Shawna, Guang Song, & Nancy M. Amato. (2005). Protein folding by motion planning. Physical Biology. 2(4). S148–S155. 36 indexed citations
13.
Lien, Jyh‐Ming, et al.. (2004). Shepherding behaviors. 4159–4164 Vol.4. 94 indexed citations
14.
Trianni, Vito, Stefano Nolfi, Marco Dorigo, et al.. (2004). Hole Avoidance: Experiments in Coordinated Motion on Rough Terrain. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 29–36. 4 indexed citations
15.
Schmidhuber, Juergen, Matteo Gagliolo, F.C.A. Groen, et al.. (2004). Bias-Optimal Incremental Learning of Control Sequences for Virtual Robots. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 658–665. 2 indexed citations
16.
Morales, Marco, et al.. (2002). Neuron Prm: A Framework for Constructing Cortical Networks. SSRN Electronic Journal. 1 indexed citations
17.
Amato, Nancy M., Michael T. Goodrich, & Edgar A. Ramos. (2000). Computing the arrangement of curve segments: divide-and-conquer algorithms via sampling. Symposium on Discrete Algorithms. 705–706. 19 indexed citations
18.
Dale, Lucia K. & Nancy M. Amato. (2000). Optimization techniques for probabilistic roadmaps. Current Research in Food Science. 7. 100551–100551. 10 indexed citations
19.
Ramos, Edgar A., Nancy M. Amato, & Michael T. Goodrich. (2000). Computing the arrangement of curve segments: Divide-and-conquer algorithms via sampling. Max Planck Institute for Plasma Physics. 705–706. 19 indexed citations
20.
Amato, Nancy M., O. Burçhan Bayazıt, Lucia K. Dale, Christopher A. Jones, & Daniel Vallejo. (1998). OBPRM: an obstacle-based PRM for 3D workspaces. 155–168. 301 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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