Jennifer E. King

416 total citations
9 papers, 245 citations indexed

About

Jennifer E. King is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Jennifer E. King has authored 9 papers receiving a total of 245 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 4 papers in Control and Systems Engineering. Recurrent topics in Jennifer E. King's work include Robotic Path Planning Algorithms (8 papers), Robot Manipulation and Learning (3 papers) and Modular Robots and Swarm Intelligence (2 papers). Jennifer E. King is often cited by papers focused on Robotic Path Planning Algorithms (8 papers), Robot Manipulation and Learning (3 papers) and Modular Robots and Swarm Intelligence (2 papers). Jennifer E. King collaborates with scholars based in United States, Germany and Italy. Jennifer E. King's co-authors include Siddhartha S Srinivasa, Tamim Asfour, Marco Cognetti, Nancy S. Pollard, Maxim Likhachev, Aaron M. Johnson, Mehmet R. Doğar, Prasanna Velagapudi, Matthew Klingensmith and Christopher M. Dellin and has published in prestigious journals such as IEEE Robotics and Automation Letters, SPIRE - Sciences Po Institutional REpository and Research Showcase @ Carnegie Mellon University (Carnegie Mellon University).

In The Last Decade

Jennifer E. King

9 papers receiving 231 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jennifer E. King United States	8	178	170	94	41	32	9	245
Ferenc Bálint-Benczédi Germany	10	103 0.6×	118 0.7×	103 1.1×	55 1.3×	18 0.6×	21	246
Jung-Su Ha South Korea	10	115 0.6×	104 0.6×	53 0.6×	54 1.3×	26 0.8×	27	216
Manuel Wüthrich Germany	9	149 0.8×	120 0.7×	101 1.1×	60 1.5×	46 1.4×	17	269
Linxi Fan United States	7	73 0.4×	124 0.7×	103 1.1×	38 0.9×	16 0.5×	12	239
Andrew Dobson United States	9	96 0.5×	160 0.9×	47 0.5×	77 1.9×	19 0.6×	11	205
Stephen James United Kingdom	9	210 1.2×	188 1.1×	133 1.4×	95 2.3×	35 1.1×	13	358
Tobia Marcucci United States	8	111 0.6×	125 0.7×	35 0.4×	28 0.7×	48 1.5×	10	205
S. Reza Ahmadzadeh United States	10	159 0.9×	68 0.4×	96 1.0×	17 0.4×	30 0.9×	24	234
Juan-Manuel Ahuactzin Mexico	6	137 0.8×	160 0.9×	36 0.4×	82 2.0×	18 0.6×	10	211
Fabien Gravot Japan	5	150 0.8×	176 1.0×	131 1.4×	34 0.8×	75 2.3×	5	281

Countries citing papers authored by Jennifer E. King

Since Specialization

Citations

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

Fields of papers citing papers by Jennifer E. King

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jennifer E. King. 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 Jennifer E. King. The network helps show where Jennifer E. King may publish in the future.

Co-authorship network of co-authors of Jennifer E. King

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

All Works

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9 of 9 papers shown

1.

King, Jennifer E., et al.. (2017). Unobservable Monte Carlo planning for nonprehensile rearrangement tasks. 4681–4688. 23 indexed citations

2.

King, Jennifer E., Marco Cognetti, & Siddhartha S Srinivasa. (2016). Rearrangement planning using object-centric and robot-centric action spaces. SPIRE - Sciences Po Institutional REpository. 3940–3947. 47 indexed citations

3.

Johnson, Aaron M., Jennifer E. King, & Siddhartha S Srinivasa. (2016). Convergent Planning. IEEE Robotics and Automation Letters. 1(2). 1044–1051. 19 indexed citations

4.

King, Jennifer E.. (2016). Robust Rearrangement Planning Using Nonprehensile Interaction. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 3 indexed citations

5.

King, Jennifer E., et al.. (2015). Nonprehensile whole arm rearrangement planning on physics manifolds. 2508–2515. 41 indexed citations

6.

King, Jennifer E., et al.. (2015). Kinodynamic randomized rearrangement planning via dynamic transitions between statically stable states. 3075–3082. 41 indexed citations

7.

King, Jennifer E., et al.. (2015). Robust trajectory selection for rearrangement planning as a multi-armed bandit problem. 2678–2685. 21 indexed citations

8.

King, Jennifer E., Matthew Klingensmith, Christopher M. Dellin, et al.. (2013). Pregrasp Manipulation as Trajectory Optimization. 37 indexed citations

9.

King, Jennifer E. & Maxim Likhachev. (2009). Efficient cost computation in cost map planning for non-circular robots. 3924–3930. 13 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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