Matteo Turchetta

1.2k total citations
10 papers, 472 citations indexed

About

Matteo Turchetta is a scholar working on Control and Systems Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matteo Turchetta has authored 10 papers receiving a total of 472 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Control and Systems Engineering, 6 papers in Artificial Intelligence and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Matteo Turchetta's work include Fault Detection and Control Systems (4 papers), Advanced Control Systems Optimization (3 papers) and Reinforcement Learning in Robotics (3 papers). Matteo Turchetta is often cited by papers focused on Fault Detection and Control Systems (4 papers), Advanced Control Systems Optimization (3 papers) and Reinforcement Learning in Robotics (3 papers). Matteo Turchetta collaborates with scholars based in Switzerland, Germany and Netherlands. Matteo Turchetta's co-authors include Andreas Krause, Felix Berkenkamp, Torsten Koller, Angela P. Schoellig, Mark G. Pfeiffer, Juan Nieto, Roland Siegwart, César Cadena, Dominik Baumann and Sebastian Trimpe and has published in prestigious journals such as Bioinformatics, IEEE Transactions on Automatic Control and Artificial Intelligence.

In The Last Decade

Matteo Turchetta

10 papers receiving 451 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Matteo Turchetta Switzerland 6 256 243 115 61 60 10 472
Melissa Greeff Canada 6 300 1.2× 188 0.8× 89 0.8× 72 1.2× 55 0.9× 15 540
Lukas Brunke Canada 5 248 1.0× 182 0.7× 49 0.4× 63 1.0× 53 0.9× 12 442
Ming-Ying Hsiao Taiwan 8 258 1.0× 137 0.6× 103 0.9× 27 0.4× 36 0.6× 24 424
Sylvia Herbert United States 7 210 0.8× 93 0.4× 104 0.9× 63 1.0× 78 1.3× 19 370
Masoud Shirzadeh Iran 10 200 0.8× 129 0.5× 74 0.6× 33 0.5× 21 0.3× 16 342
Jeremy Gillula United States 9 310 1.2× 151 0.6× 176 1.5× 111 1.8× 102 1.7× 12 546
Somil Bansal United States 8 154 0.6× 72 0.3× 81 0.7× 64 1.0× 65 1.1× 25 319
Kim P. Wabersich Switzerland 10 593 2.3× 134 0.6× 74 0.6× 109 1.8× 52 0.9× 22 775
Felix Berkenkamp Switzerland 10 350 1.4× 269 1.1× 37 0.3× 56 0.9× 94 1.6× 15 557
Ashkan Jasour United States 10 116 0.5× 84 0.3× 129 1.1× 96 1.6× 39 0.7× 30 321

Countries citing papers authored by Matteo Turchetta

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

10 of 10 papers shown
1.
Köhler, Johannes, et al.. (2025). Safe Guaranteed Exploration for Nonlinear Systems. IEEE Transactions on Automatic Control. 70(8). 5333–5348. 2 indexed citations
2.
Turchetta, Matteo, Daniel Ariza-Suárez, Steven Yates, et al.. (2023). ChromaX: a fast and scalable breeding program simulator. Bioinformatics. 39(12). 4 indexed citations
3.
Turchetta, Matteo, et al.. (2023). GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems. Artificial Intelligence. 320. 103922–103922. 6 indexed citations
4.
Turchetta, Matteo, Andrey Kolobov, Shital Shah, Andreas Krause, & Alekh Agarwal. (2020). Safe Reinforcement Learning via Curriculum Induction. Neural Information Processing Systems. 33. 12151–12162. 2 indexed citations
5.
Turchetta, Matteo, Felix Berkenkamp, & Andreas Krause. (2019). Safe Exploration for Interactive Machine Learning. arXiv (Cornell University). 32. 2887–2897. 10 indexed citations
6.
Koller, Torsten, et al.. (2019). Learning-based Model Predictive Control for Safe Reinforcement Learning. 1 indexed citations
7.
Pfeiffer, Mark G., Matteo Turchetta, César Cadena, et al.. (2018). Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations. IEEE Robotics and Automation Letters. 3(4). 4423–4430. 122 indexed citations
8.
Koller, Torsten, Felix Berkenkamp, Matteo Turchetta, & Andreas Krause. (2018). Learning-Based Model Predictive Control for Safe Exploration. 6059–6066. 171 indexed citations
9.
Berkenkamp, Felix, Matteo Turchetta, Angela P. Schoellig, & Andreas Krause. (2017). Safe Model-based Reinforcement Learning with Stability Guarantees. arXiv (Cornell University). 30. 908–918. 137 indexed citations
10.
Turchetta, Matteo, Felix Berkenkamp, & Andreas Krause. (2016). Safe Exploration in Finite Markov Decision Processes with Gaussian Processes. arXiv (Cornell University). 29. 4312–4320. 17 indexed citations

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.

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