Eric Undersander
Impact in
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- Multimodal Machine Learning Applications
- Robotic Path Planning Algorithms
- Human Pose and Action Recognition
- Advanced Neural Network Applications
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- Robot Manipulation and Learning
Papers in
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- Multimodal Machine Learning Applications 2
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- Reinforcement Learning in Robotics 3
- AI-based Problem Solving and Planning 1
- Co-authors
- Dhruv Batra (4 shared papers)Abhishek Das (1 shared paper)Akshara Rai (2 shared papers)Alexander Clegg (2 shared papers)Naoki Yokoyama (1 shared paper)Tsung-Yen Yang (1 shared paper)Sehoon Ha (1 shared paper)Hanxiao Jiang (1 shared paper)
- Journals
- IEEE Robotics and Automation Letters (2 papers)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Eric Undersander
5 papers receiving 116 citations
Peers
Comparison fields: 5 of 34
- Computer Vision and Pattern Recognition 72
- Control and Systems Engineering 42
- Artificial Intelligence 47
- Aerospace Engineering 19
- Industrial and Manufacturing Engineering 7
Countries citing papers authored by Eric Undersander
This map shows the geographic impact of Eric Undersander'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 Eric Undersander with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Undersander more than expected).
Fields of papers citing papers by Eric Undersander
This network shows the impact of papers produced by Eric Undersander. 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 Eric Undersander. The network helps show where Eric Undersander may publish in the future.
Co-authors
The 14 scholars most cited alongside Eric Undersander, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 53 | |
| 2 | 2022 | 34 | |
| 3 | 2023 | 17 | |
| 4 | 2024 | 13 | |
| 5 | 2023 | 1 |
About Eric Undersander
Eric Undersander is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Aerospace Engineering and Environmental Engineering, having authored 5 papers that have together received 118 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Robotics and Sensor-Based Localization (2 papers), Multimodal Machine Learning Applications (2 papers), Robot Manipulation and Learning (2 papers), Modular Robots and Swarm Intelligence (1 paper), Remote Sensing and LiDAR Applications (1 paper), 3D Surveying and Cultural Heritage (1 paper) and AI-based Problem Solving and Planning (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (72 citations), Control and Systems Engineering (42 citations), Artificial Intelligence (47 citations), Aerospace Engineering (19 citations) and Industrial and Manufacturing Engineering (7 citations). Eric Undersander has collaborated with scholars based in United States and Canada. Frequent co-authors include Dhruv Batra, Abhishek Das, Akshara Rai, Alexander Clegg, Naoki Yokoyama, Tsung-Yen Yang, Sehoon Ha, Hanxiao Jiang, Anne Lynn S. Chang and Manolis Savva. Their work appears in journals such as IEEE Robotics and Automation Letters and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.