Felipe Leno da Silva
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering
- Control and Systems Engineering top 10%
- Automotive Engineering top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Anna Helena Reali CostaRuben GlattDiederik M. RoijersTiago Maurício FrancoyPeter StoneMatthew E. TaylorGarrett WarnellCíntia Borges Margi
- Topics
- Reinforcement Learning in Robotics (19 papers)Data Stream Mining Techniques (6 papers)Evolutionary Algorithms and Applications (5 papers)
- Partner nations
- BrazilUnited StatesAustria
In The Last Decade
Felipe Leno da Silva
32 papers receiving 684 citations
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 341
- Electrical and Electronic Engineering 181
- Control and Systems Engineering 138
- Automotive Engineering 113
- Computer Networks and Communications 81
Countries citing papers authored by Felipe Leno da Silva
This map shows the geographic impact of Felipe Leno da Silva'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 Felipe Leno da Silva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Leno da Silva more than expected).
Fields of papers citing papers by Felipe Leno da Silva
This network shows the impact of papers produced by Felipe Leno da Silva. 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 Felipe Leno da Silva. The network helps show where Felipe Leno da Silva may publish in the future.
Co-authorship network of co-authors of Felipe Leno da Silva
This figure shows the co-authorship network connecting the top 25 collaborators of Felipe Leno da Silva. A scholar is included among the top collaborators of Felipe Leno da Silva 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 Felipe Leno da Silva. Felipe Leno da Silva 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 | 1 | |
| 3 | 4 | |
| 4 | 6 | |
| 5 | 20 | |
| 6 | 31 | |
| 7 | 1 | |
| 8 | 163 | |
| 9 | 2 | |
| 10 | 16 | |
| 11 | 20 | |
| 12 | 1 | |
| 13 | Using Options to Accelerate Learning of New Tasks According to Human Preferences. | 1 |
| 14 | 56 | |
| 15 | 0 | |
| 16 | 1 | |
| 17 | A Framework for Scalable Inference of Temporal Gene Regulatory Networks based on Clustering and Multivariate Analysis. | 1 |
| 18 | 23 | |
| 19 | 3 | |
| 20 | 40 |
About Felipe Leno da Silva
Felipe Leno da Silva is a scholar working on Artificial Intelligence, Control and Systems Engineering and Management Science and Operations Research, having authored 33 papers that have together received 712 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (19 papers), Data Stream Mining Techniques (6 papers) and Evolutionary Algorithms and Applications (5 papers). The work is most often cited by research in Artificial Intelligence (341 citations), Automotive Engineering (113 citations) and Control and Systems Engineering (138 citations). Felipe Leno da Silva has collaborated with scholars based in Brazil, United States and Austria. Frequent co-authors include Anna Helena Reali Costa, Ruben Glatt, Diederik M. Roijers, Tiago Maurício Francoy, Peter Stone, Matthew E. Taylor, Garrett Warnell, Cíntia Borges Margi, Pablo Hernández-Leal and Renan C. A. Alves. Their work appears in journals such as Expert Systems with Applications, IEEE Access and IEEE Transactions on Smart Grid.
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.