Tomás Prieto-Rumeau

735 total citations
43 papers, 435 citations indexed

About

Tomás Prieto-Rumeau is a scholar working on Artificial Intelligence, Management Science and Operations Research and Statistics and Probability. According to data from OpenAlex, Tomás Prieto-Rumeau has authored 43 papers receiving a total of 435 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 20 papers in Management Science and Operations Research and 14 papers in Statistics and Probability. Recurrent topics in Tomás Prieto-Rumeau's work include Reinforcement Learning in Robotics (17 papers), Markov Chains and Monte Carlo Methods (14 papers) and Advanced Control Systems Optimization (9 papers). Tomás Prieto-Rumeau is often cited by papers focused on Reinforcement Learning in Robotics (17 papers), Markov Chains and Monte Carlo Methods (14 papers) and Advanced Control Systems Optimization (9 papers). Tomás Prieto-Rumeau collaborates with scholars based in Spain, Mexico and France. Tomás Prieto-Rumeau's co-authors include Onésimo Hernández–Lerma, François Dufour, Jean B. Lasserre, Mihail Zervos, José‐Luis Menaldi, Xi‐Ren Cao, Qiying Hu, Xianping Guo, Mark Edward Lewis and Quanxin Zhu and has published in prestigious journals such as IEEE Transactions on Automatic Control, Journal of Mathematical Analysis and Applications and Systems & Control Letters.

In The Last Decade

Tomás Prieto-Rumeau

37 papers receiving 386 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tomás Prieto-Rumeau Spain 13 188 158 115 103 101 43 435
Alexey Piunovskiy United Kingdom 13 145 0.8× 142 0.9× 121 1.1× 71 0.7× 79 0.8× 54 506
Masami Kurano Japan 16 264 1.4× 234 1.5× 84 0.7× 255 2.5× 87 0.9× 59 544
Rolando Cavazos–Cadena Mexico 16 253 1.3× 280 1.8× 190 1.7× 236 2.3× 145 1.4× 84 720
Karl Hinderer Germany 9 182 1.0× 126 0.8× 81 0.7× 79 0.8× 92 0.9× 26 525
Daniel Hernández–Hernández Mexico 14 373 2.0× 143 0.9× 143 1.2× 107 1.0× 382 3.8× 50 755
A. A. Yushkevich United States 11 86 0.5× 86 0.5× 64 0.6× 85 0.8× 73 0.7× 22 302
Łukasz Stettner Poland 14 107 0.6× 47 0.3× 120 1.0× 73 0.7× 252 2.5× 72 487
Jun-ichi Nakagami Japan 12 211 1.1× 144 0.9× 30 0.3× 202 2.0× 52 0.5× 34 334
R. Syski United States 8 99 0.5× 55 0.3× 48 0.4× 91 0.9× 22 0.2× 27 418
Susan M. Pitts United Kingdom 12 256 1.4× 103 0.7× 14 0.1× 183 1.8× 128 1.3× 33 587

Countries citing papers authored by Tomás Prieto-Rumeau

Since Specialization
Citations

This map shows the geographic impact of Tomás Prieto-Rumeau'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 Tomás Prieto-Rumeau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomás Prieto-Rumeau more than expected).

Fields of papers citing papers by Tomás Prieto-Rumeau

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tomás Prieto-Rumeau. 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 Tomás Prieto-Rumeau. The network helps show where Tomás Prieto-Rumeau may publish in the future.

Co-authorship network of co-authors of Tomás Prieto-Rumeau

This figure shows the co-authorship network connecting the top 25 collaborators of Tomás Prieto-Rumeau. A scholar is included among the top collaborators of Tomás Prieto-Rumeau 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 Tomás Prieto-Rumeau. Tomás Prieto-Rumeau is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Franco, Daniel, et al.. (2025). Finding the Optimal Rate of Dispersal for a Population: A Markov Decision Process Approach. SIAM Journal on Applied Mathematics. 85(6). 2543–2565.
2.
Dufour, François & Tomás Prieto-Rumeau. (2024). Absorbing Markov decision processes. ESAIM Control Optimisation and Calculus of Variations. 30. 5–5. 4 indexed citations
3.
Anselmi, Jonatha, François Dufour, & Tomás Prieto-Rumeau. (2016). Computable approximations for continuous-time Markov decision processes on Borel spaces based on empirical measures. Journal of Mathematical Analysis and Applications. 443(2). 1323–1361. 4 indexed citations
4.
Dufour, François & Tomás Prieto-Rumeau. (2015). Conditions for the Solvability of the Linear Programming Formulation for Constrained Discounted Markov Decision Processes. Applied Mathematics & Optimization. 74(1). 27–51. 5 indexed citations
5.
Prieto-Rumeau, Tomás, et al.. (2014). Approximation of two-person zero-sum continuous-time Markov games with average payoff criterion. Operations Research Letters. 43(1). 110–116. 2 indexed citations
6.
Dufour, François & Tomás Prieto-Rumeau. (2013). Stochastic approximations of constrained discounted Markov decision processes. Journal of Mathematical Analysis and Applications. 413(2). 856–879. 11 indexed citations
7.
Prieto-Rumeau, Tomás & Onésimo Hernández–Lerma. (2012). Uniform ergodicity of continuous-time controlled Markov chains: A survey and new results. Annals of Operations Research. 241(1-2). 249–293. 6 indexed citations
8.
Prieto-Rumeau, Tomás & Onésimo Hernández–Lerma. (2011). Selected Topics on Continuous-Time Controlled Markov Chains and Markov Games. 29 indexed citations
9.
Dufour, François & Tomás Prieto-Rumeau. (2011). Approximation of Markov decision processes with general state space. Journal of Mathematical Analysis and Applications. 388(2). 1254–1267. 21 indexed citations
10.
Prieto-Rumeau, Tomás & Onésimo Hernández–Lerma. (2010). The vanishing discount approach to constrained continuous-time controlled Markov chains. Systems & Control Letters. 59(8). 504–509. 10 indexed citations
11.
Prieto-Rumeau, Tomás, et al.. (2009). De Finetti-type theorems for random selection processes. Necessary and sufficient conditions. Journal of Mathematical Analysis and Applications. 365(1). 198–209.
12.
Prieto-Rumeau, Tomás, et al.. (2009). Approximating Ergodic Average Reward Continuous-Time Controlled Markov Chains. IEEE Transactions on Automatic Control. 55(1). 201–207. 14 indexed citations
13.
Prieto-Rumeau, Tomás, et al.. (2009). De Finetti's-type results for some families of non identically distributed random variables. Electronic Journal of Probability. 14(none). 2 indexed citations
14.
Zhu, Quanxin & Tomás Prieto-Rumeau. (2008). Bias and Overtaking Optimality for Continuous-Time Jump Markov Decision Processes in Polish Spaces. Journal of Applied Probability. 45(2). 417–429. 10 indexed citations
15.
Prieto-Rumeau, Tomás, et al.. (2008). A De Finetti-type theorem for nonexchangeable finite-valued random variables. Journal of Mathematical Analysis and Applications. 347(2). 407–415. 3 indexed citations
16.
Prieto-Rumeau, Tomás & Onésimo Hernández–Lerma. (2006). Bias Optimality for Continuous-Time Controlled Markov Chains. SIAM Journal on Control and Optimization. 45(1). 51–73. 22 indexed citations
17.
Lasserre, Jean B., Tomás Prieto-Rumeau, & Mihail Zervos. (2006). PRICING A CLASS OF EXOTIC OPTIONS VIA MOMENTS AND SDP RELAXATIONS. Mathematical Finance. 16(3). 469–494. 34 indexed citations
18.
Guo, Xianping, Onésimo Hernández–Lerma, Tomás Prieto-Rumeau, et al.. (2006). A survey of recent results on continuous-time Markov decision processes. Top. 14(2). 177–261. 54 indexed citations
19.
Prieto-Rumeau, Tomás, et al.. (2004). A general approach to continuous-time discounted Markov control processes. 1–41. 1 indexed citations
20.

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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