Nadia Figueroa
- Human-Computer Interaction top 5%
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- Robot Manipulation and Learning 16
- Robotic Mechanisms and Dynamics 5
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- Robotic Path Planning Algorithms 8
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- Muscle activation and electromyography studies 6
- Prosthetics and Rehabilitation Robotics 4
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- Motor Control and Adaptation 6
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- Robotics and Sensor-Based Localization 7
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- 3D Surveying and Cultural Heritage 4
- Co-authors
- Aude BillardHaiwei DongAbdulmotaleb El SaddikSeyed Sina Mirrazavi SalehianNikolaos MavridisAli DaneshShen LiLishuai Jin
- Cited by
- Human-Computer InteractionControl and Systems EngineeringComputer Vision and Pattern Recognition
- Journals
- SHILAP Revista de lepidopterología (1 paper)IEEE Access (1 paper)Sensors (1 paper)
- Partner nations
- United StatesSwitzerlandCanada
In The Last Decade
Nadia Figueroa
40 papers receiving 614 citations
Peers
Comparison fields: 5 of 76
- Human-Computer Interaction 85
- Control and Systems Engineering 312
- Computer Vision and Pattern Recognition 193
- Biomedical Engineering 205
- Cognitive Neuroscience 85
Countries citing papers authored by Nadia Figueroa
This map shows the geographic impact of Nadia Figueroa'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 Nadia Figueroa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nadia Figueroa more than expected).
Fields of papers citing papers by Nadia Figueroa
This network shows the impact of papers produced by Nadia Figueroa. 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 Nadia Figueroa. The network helps show where Nadia Figueroa may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Nadia Figueroa, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 2 | |
| 9 | 2024 | 2 | |
| 10 | 2024 | 0 | |
| 11 | 2023 | 32 | |
| 12 | 2022 | 0 | |
| 13 | 2022 | 15 | |
| 14 | 2018 | 18 | |
| 15 | A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning. | 2018 | 15 |
| 16 | Learning Complex Manipulation Tasks from Heterogeneous and Unstructured Demonstrations | 2017 | 5 |
| 17 | 2016 | 17 | |
| 18 | 2016 | 13 | |
| 19 | 2014 | 10 | |
| 20 | 2013 | 14 |
About Nadia Figueroa
Nadia Figueroa is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Human-Computer Interaction, having authored 49 papers that have together received 634 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (16 papers), Robotic Path Planning Algorithms (8 papers), Robotics and Sensor-Based Localization (7 papers), Motor Control and Adaptation (6 papers), Muscle activation and electromyography studies (6 papers), Robotic Mechanisms and Dynamics (5 papers), Prosthetics and Rehabilitation Robotics (4 papers) and 3D Surveying and Cultural Heritage (4 papers). The work is most often cited by research in Human-Computer Interaction (85 citations), Control and Systems Engineering (312 citations) and Computer Vision and Pattern Recognition (193 citations). Nadia Figueroa has collaborated with scholars based in United States, Switzerland and Canada. Frequent co-authors include Aude Billard, Haiwei Dong, Abdulmotaleb El Saddik, Seyed Sina Mirrazavi Salehian, Nikolaos Mavridis, Ali Danesh, Shen Li, Lishuai Jin, Yueying Yang and Sebastian Lee. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.
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.