Matteo Turchetta
- Control and Systems Engineering top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Automotive Engineering top 10%
- Computational Theory and Mathematics top 10%
- Co-authors
- Andreas KrauseFelix BerkenkampTorsten KollerAngela P. SchoelligRoland SiegwartJuan NietoCésar CadenaMark G. Pfeiffer
- Topics
- Fault Detection and Control Systems (4 papers)Advanced Control Systems Optimization (3 papers)Reinforcement Learning in Robotics (3 papers)
- Cited by
- Control and Systems EngineeringArtificial IntelligenceComputer Vision and Pattern Recognition
- Partner nations
- SwitzerlandGermanyNetherlands
In The Last Decade
Matteo Turchetta
10 papers receiving 451 citations
Peers
Comparison fields: 5 of 48
- Control and Systems Engineering 256
- Artificial Intelligence 243
- Computer Vision and Pattern Recognition 115
- Automotive Engineering 61
- Computational Theory and Mathematics 60
Countries citing papers authored by Matteo Turchetta
This map shows the geographic impact of Matteo Turchetta'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 Matteo Turchetta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matteo Turchetta more than expected).
Fields of papers citing papers by Matteo Turchetta
This network shows the impact of papers produced by Matteo Turchetta. 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 Matteo Turchetta. The network helps show where Matteo Turchetta may publish in the future.
Co-authorship network of co-authors of Matteo Turchetta
This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Turchetta. A scholar is included among the top collaborators of Matteo Turchetta 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 Matteo Turchetta. Matteo Turchetta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 4 | |
| 3 | 6 | |
| 4 | Safe Reinforcement Learning via Curriculum Induction | 2 |
| 5 | 10 | |
| 6 | Learning-based Model Predictive Control for Safe Reinforcement Learning | 1 |
| 7 | 122 | |
| 8 | 171 | |
| 9 | 137 | |
| 10 | Safe Exploration in Finite Markov Decision Processes with Gaussian Processes | 17 |
About Matteo Turchetta
Matteo Turchetta is a scholar working on Control and Systems Engineering, Artificial Intelligence and Safety, Risk, Reliability and Quality, having authored 10 papers that have together received 472 indexed citations. Recurring topics across this work include Fault Detection and Control Systems (4 papers), Advanced Control Systems Optimization (3 papers) and Reinforcement Learning in Robotics (3 papers). The work is most often cited by research in Control and Systems Engineering (256 citations), Artificial Intelligence (243 citations) and Computer Vision and Pattern Recognition (115 citations). Matteo Turchetta has collaborated with scholars based in Switzerland, Germany and Netherlands. Frequent co-authors include Andreas Krause, Felix Berkenkamp, Torsten Koller, Angela P. Schoellig, Roland Siegwart, Juan Nieto, César Cadena, Mark G. Pfeiffer, Sebastian Trimpe and Dominik Baumann. Their work appears in journals such as Bioinformatics, IEEE Transactions on Automatic Control and Artificial Intelligence.
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.