Chris Paxton

1.9k citations
41 papers · 930 · h-index 18

Impact in

Papers in

Chris Paxton

38 papers receiving 895 citations

Peers

Chris Paxton
Comparison fields: 5 of 77
  • Control and Systems Engineering 428
  • Computer Vision and Pattern Recognition 377
  • Artificial Intelligence 371
  • Human-Computer Interaction 60
  • Industrial and Manufacturing Engineering 79
Replace Valts Blukis with:
Valts Blukis United States
Pete Florence United States
Hangxin Liu China
Athanasios Polydoros Denmark
Przemyslaw A. Lasota United States
Lars Kunze United Kingdom
Brian Ichter United States
Wim Meeussen United States
Miguel Hernando Spain
Alberto Finzi Italy
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Citations per field
00.5×8.3×
Valts Blukis · 1×
Citations per year

Countries citing papers authored by Chris Paxton

Since Specialization
Citations

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

Fields of papers citing papers by Chris Paxton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017109
2 202382
3 201569
4 201758
5
Developing predictive models using electronic medical records: challenges and pitfalls.
201357
6 202155
7 202351
8 202242
9 202037
10 201536
11 202235
12 201929
13 202426
14 202026
15 202225
16 202423
17 202122
18 202319
19 201614
20 202214

About Chris Paxton

Chris Paxton is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering and Social Psychology, having authored 41 papers that have together received 930 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (21 papers), Robotic Path Planning Algorithms (12 papers), Reinforcement Learning in Robotics (10 papers), Multimodal Machine Learning Applications (9 papers), Robotics and Sensor-Based Localization (7 papers), AI-based Problem Solving and Planning (6 papers), Human Pose and Action Recognition (5 papers) and Robotics and Automated Systems (3 papers). The work is most often cited by research in Control and Systems Engineering (428 citations), Computer Vision and Pattern Recognition (377 citations), Artificial Intelligence (371 citations), Human-Computer Interaction (60 citations) and Industrial and Manufacturing Engineering (79 citations). Chris Paxton has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Gregory D. Hager, Dieter Fox, Maya Çakmak, Shiqi Zhang, Xiaohan Zhang, Yan Ding, Suchi Saria, Alexandru Niculescu-Mizil, Tucker Hermans and Marin Kobilarov. Their work appears in journals such as Critical Care Medicine, IEEE Robotics and Automation Letters, Advanced Robotics, The International Journal of Robotics Research and 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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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