Matthew Matl
Impact in
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- Robot Manipulation and Learning
- Hardware and Architecture top 5%
- Parallel Computing and Optimization Techniques
Papers in
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- Interconnection Networks and Systems 5
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- Robot Manipulation and Learning 4
- Co-authors
- Jeffrey Mahler (5 shared papers)Michael Danielczuk (3 shared papers)Ken Goldberg (5 shared papers)Vishal Satish (1 shared paper)Stephen McKinley (1 shared paper)Albert P. Li (1 shared paper)David V. Gealy (1 shared paper)Xinyu Liu (1 shared paper)
- Journals
- ACM SIGPLAN Notices (1 paper)Science Robotics (1 paper)Communications of the ACM (1 paper)ACM SIGOPS Operating Systems Review (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Matthew Matl
11 papers receiving 896 citations
Matthew Matl's Hit Papers
Peers
Comparison fields: 5 of 68
- Control and Systems Engineering 590
- Hardware and Architecture 135
- Human-Computer Interaction 88
- Computer Vision and Pattern Recognition 226
- Industrial and Manufacturing Engineering 90
Countries citing papers authored by Matthew Matl
This map shows the geographic impact of Matthew Matl'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 Matthew Matl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Matl more than expected).
Fields of papers citing papers by Matthew Matl
This network shows the impact of papers produced by Matthew Matl. 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 Matthew Matl. The network helps show where Matthew Matl may publish in the future.
Co-authors
The 25 scholars most cited alongside Matthew Matl, 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 | Learning ambidextrous robot grasping policies Hit paper breakdown → | 2019 | 379 |
| 2 | 2018 | 211 | |
| 3 | 2019 | 122 | |
| 4 | 2016 | 115 | |
| 5 | 2016 | 39 | |
| 6 | 2017 | 26 | |
| 7 | 2016 | 19 | |
| 8 | Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds. | 2018 | 11 |
| 9 | 2016 | 6 | |
| 10 | 2019 | 4 | |
| 11 | 2017 | 1 |
About Matthew Matl
Matthew Matl is a scholar working on Computer Networks and Communications, Control and Systems Engineering, Hardware and Architecture, Biomedical Engineering and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 933 indexed citations. Recurring topics across this work include Embedded Systems Design Techniques (5 papers), Parallel Computing and Optimization Techniques (5 papers), Interconnection Networks and Systems (5 papers), Robot Manipulation and Learning (4 papers), Soft Robotics and Applications (3 papers), Advanced Neural Network Applications (2 papers), 3D Shape Modeling and Analysis (2 papers) and 3D Surveying and Cultural Heritage (1 paper). The work is most often cited by research in Control and Systems Engineering (590 citations), Hardware and Architecture (135 citations), Human-Computer Interaction (88 citations), Computer Vision and Pattern Recognition (226 citations) and Industrial and Manufacturing Engineering (90 citations). Matthew Matl has collaborated with scholars based in United States and China. Frequent co-authors include Jeffrey Mahler, Michael Danielczuk, Ken Goldberg, Vishal Satish, Stephen McKinley, Albert P. Li, David V. Gealy, Xinyu Liu, Andrew C. Li and Andrew Lee. Their work appears in journals such as ACM SIGPLAN Notices, Science Robotics, Communications of the ACM, ACM SIGOPS Operating Systems Review and arXiv (Cornell University).
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.