Bruno Lacerda
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 5%
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering
- Software top 5%
- Co-authors
- Nick HawesDavid ParkerPedro U. LimaPaul DuckworthMarc HanheideJaime Pulido FentanesTomáš KrajníkJeremy Wyatt
- Topics
- Formal Methods in Verification (18 papers)Reinforcement Learning in Robotics (11 papers)Petri Nets in System Modeling (9 papers)
- Journals
- IEEE Transactions on Automatic ControlThe International Journal of Robotics ResearchIEEE Transactions on Robotics
- Partner nations
- United KingdomPortugalUnited States
In The Last Decade
Bruno Lacerda
40 papers receiving 373 citations
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 190
- Computational Theory and Mathematics 147
- Computer Vision and Pattern Recognition 122
- Control and Systems Engineering 61
- Software 57
Countries citing papers authored by Bruno Lacerda
This map shows the geographic impact of Bruno Lacerda'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 Lacerda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bruno Lacerda more than expected).
Fields of papers citing papers by Bruno Lacerda
This network shows the impact of papers produced by Bruno Lacerda. 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 Lacerda. The network helps show where Bruno Lacerda may publish in the future.
Co-authorship network of co-authors of Bruno Lacerda
This figure shows the co-authorship network connecting the top 25 collaborators of Bruno Lacerda. A scholar is included among the top collaborators of Bruno Lacerda 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 Bruno Lacerda. Bruno Lacerda 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 | 2 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 2 | |
| 8 | 14 | |
| 9 | Active inference for integrated state-estimation, control, and learning | 14 |
| 10 | 1 | |
| 11 | 18 | |
| 12 | Time-Bounded Mission Planning in Time-Varying Domains with Semi-MDPs and Gaussian Processes | 3 |
| 13 | 9 | |
| 14 | 1 | |
| 15 | 26 | |
| 16 | 42 | |
| 17 | Optimal policy generation for partially satisfiable co-safe LTL specifications | 24 |
| 18 | 51 | |
| 19 | 1 | |
| 20 | 2 |
About Bruno Lacerda
Bruno Lacerda is a scholar working on Software, Computational Theory and Mathematics and Hardware and Architecture, having authored 41 papers that have together received 386 indexed citations. Recurring topics across this work include Formal Methods in Verification (18 papers), Reinforcement Learning in Robotics (11 papers) and Petri Nets in System Modeling (9 papers). The work is most often cited by research in Software (57 citations), Computational Theory and Mathematics (147 citations) and Hardware and Architecture (44 citations). Bruno Lacerda has collaborated with scholars based in United Kingdom, Portugal and United States. Frequent co-authors include Nick Hawes, David Parker, Pedro U. Lima, Paul Duckworth, Marc Hanheide, Jaime Pulido Fentanes, Tomáš Krajník, Jeremy Wyatt, Manuel Mühlig and Jana Tůmová. Their work appears in journals such as IEEE Transactions on Automatic Control, The International Journal of Robotics Research and IEEE Transactions on Robotics.
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.