Bruno C. da Silva
- Software top 10%
- Model-Driven Software Engineering Techniques 3
- Transportation top 10%
- Information Systems top 10%
- Software Engineering Research 8
- Software Engineering Techniques and Practices 5
- Service-Oriented Architecture and Web Services 3
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- Open Source Software Innovations 4
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics 8
- Advanced Software Engineering Methodologies 5
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- Advanced Bandit Algorithms Research 5
- Co-authors
- Ana L. C. BazzanPaulo Martins EngelPaulo MacielCláudio Sant’AnnaKleinner FariasChristina ChávezMatjaž PercErivelton G. Nepomuceno
- Journals
- Applied Mathematics and Computation (1 paper)Information and Software Technology (2 papers)ACM SIGMETRICS Performance Evaluation Review (1 paper)
- Partner nations
- BrazilUnited StatesAustria
In The Last Decade
Bruno C. da Silva
23 papers receiving 265 citations
Peers
Comparison fields: 5 of 59
- Software 34
- Transportation 29
- Information Systems 87
- Computer Science Applications 20
- Artificial Intelligence 118
Countries citing papers authored by Bruno C. da Silva
This map shows the geographic impact of Bruno C. 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 Bruno C. 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 Bruno C. da Silva more than expected).
Fields of papers citing papers by Bruno C. da Silva
This network shows the impact of papers produced by Bruno C. 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 Bruno C. da Silva. The network helps show where Bruno C. da Silva may publish in the future.
Co-authorship network
The 22 scholars most cited alongside Bruno C. da Silva, 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 | 2021 | 17 | |
| 2 | Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods | 2021 | 1 |
| 3 | 2019 | 1 | |
| 4 | 2019 | 0 | |
| 5 | 2019 | 7 | |
| 6 | 2019 | 1 | |
| 7 | 2018 | 27 | |
| 8 | 2018 | 11 | |
| 9 | 2018 | 5 | |
| 10 | 2018 | 5 | |
| 11 | 2018 | 7 | |
| 12 | 2017 | 4 | |
| 13 | 2017 | 12 | |
| 14 | 2016 | 2 | |
| 15 | 2014 | 3 | |
| 16 | 2013 | 12 | |
| 17 | 2010 | 42 | |
| 18 | 2009 | 0 | |
| 19 | 2006 | 97 | |
| 20 | 2006 | 6 |
About Bruno C. da Silva
Bruno C. da Silva is a scholar working on Software, Computer Science Applications and Artificial Intelligence, having authored 25 papers that have together received 278 indexed citations. Recurring topics across this work include Software Engineering Research (8 papers), Reinforcement Learning in Robotics (8 papers), Advanced Software Engineering Methodologies (5 papers), Software Engineering Techniques and Practices (5 papers), Advanced Bandit Algorithms Research (5 papers), Open Source Software Innovations (4 papers), Model-Driven Software Engineering Techniques (3 papers) and Service-Oriented Architecture and Web Services (3 papers). The work is most often cited by research in Software (34 citations), Transportation (29 citations) and Information Systems (87 citations). Bruno C. da Silva has collaborated with scholars based in Brazil, United States and Austria. Frequent co-authors include Ana L. C. Bazzan, Paulo Martins Engel, Paulo Maciel, Cláudio Sant’Anna, Kleinner Farias, Christina Chávez, Matjaž Perc, Erivelton G. Nepomuceno, Manish Marwah and Tom Christian. Their work appears in journals such as Applied Mathematics and Computation, Information and Software Technology and ACM SIGMETRICS Performance Evaluation Review.
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.