Diego Romeres
- Control and Systems Engineering top 5%
- Artificial Intelligence
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Cognitive Neuroscience
- Co-authors
- Devesh K. JhaDaniel NikovskiAlberto RodríguezFrancesco BulloFlorian DörflerSiyuan DongArvind U. RaghunathanMasayoshi Tomizuka
- Topics
- Robot Manipulation and Learning (16 papers)Gaussian Processes and Bayesian Inference (10 papers)Advanced Control Systems Optimization (10 papers)
- Cited by
- Control and Systems EngineeringIndustrial and Manufacturing EngineeringArtificial Intelligence
- Journals
- IEEE Transactions on RoboticsEngineering Applications of Artificial IntelligenceIEEE Control Systems
- Partner nations
- United StatesItalyJapan
In The Last Decade
Diego Romeres
41 papers receiving 327 citations
Peers
Comparison fields: 5 of 48
- Control and Systems Engineering 224
- Artificial Intelligence 81
- Biomedical Engineering 60
- Computer Vision and Pattern Recognition 46
- Cognitive Neuroscience 44
Countries citing papers authored by Diego Romeres
This map shows the geographic impact of Diego Romeres'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 Diego Romeres with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Romeres more than expected).
Fields of papers citing papers by Diego Romeres
This network shows the impact of papers produced by Diego Romeres. 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 Diego Romeres. The network helps show where Diego Romeres may publish in the future.
Co-authorship network of co-authors of Diego Romeres
This figure shows the co-authorship network connecting the top 25 collaborators of Diego Romeres. A scholar is included among the top collaborators of Diego Romeres 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 Diego Romeres. Diego Romeres is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 11 | |
| 4 | 2 | |
| 5 | 5 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 2 | |
| 11 | 13 | |
| 12 | 0 | |
| 13 | 8 | |
| 14 | PYROBOCOP: Python-based Robotic Control & Optimization Package for Manipulation | 2 |
| 15 | 0 | |
| 16 | 12 | |
| 17 | 19 | |
| 18 | 11 | |
| 19 | 15 | |
| 20 | 17 |
About Diego Romeres
Diego Romeres is a scholar working on Control and Systems Engineering, Industrial and Manufacturing Engineering and Artificial Intelligence, having authored 47 papers that have together received 345 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (16 papers), Gaussian Processes and Bayesian Inference (10 papers) and Advanced Control Systems Optimization (10 papers). The work is most often cited by research in Control and Systems Engineering (224 citations), Industrial and Manufacturing Engineering (34 citations) and Artificial Intelligence (81 citations). Diego Romeres has collaborated with scholars based in United States, Italy and Japan. Frequent co-authors include Devesh K. Jha, Daniel Nikovski, Alberto Rodríguez, Francesco Bullo, Florian Dörfler, Siyuan Dong, Arvind U. Raghunathan, Masayoshi Tomizuka, Ruggero Carli and Siddarth Jain. Their work appears in journals such as IEEE Transactions on Robotics, Engineering Applications of Artificial Intelligence and IEEE Control Systems.
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.