Ufuk Topcu

667 total papers · 9.9k total citations
268 papers, 5.7k citations indexed

About

Ufuk Topcu is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Ufuk Topcu has authored 268 papers receiving a total of 5.7k indexed citations (citations by other indexed papers that have themselves been cited), including 109 papers in Computational Theory and Mathematics, 105 papers in Artificial Intelligence and 81 papers in Control and Systems Engineering. Recurrent topics in Ufuk Topcu's work include Formal Methods in Verification (93 papers), Reinforcement Learning in Robotics (39 papers) and Advanced Control Systems Optimization (29 papers). Ufuk Topcu is often cited by papers focused on Formal Methods in Verification (93 papers), Reinforcement Learning in Robotics (39 papers) and Advanced Control Systems Optimization (29 papers). Ufuk Topcu collaborates with scholars based in United States, Germany and Netherlands. Ufuk Topcu's co-authors include Steven H. Low, Richard M. Murray, Lingwen Gan, Tichakorn Wongpiromsarn, Dennice F. Gayme, Changhong Zhao, Andrew Packard, Na Li, Peter Seiler and Eric M. Wolff and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Automatic Control and Scientific Reports.

In The Last Decade

Ufuk Topcu

250 papers receiving 5.6k citations

Hit Papers

Optimal decentralized pro... 2013 2026 2017 2021 2013 2014 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Ufuk Topcu 2.3k 2.1k 1.5k 1.1k 1.0k 268 5.7k
Reinhard German 2.4k 1.0× 1.1k 0.5× 510 0.3× 416 0.4× 947 0.9× 260 4.5k
Matthias Althoff 604 0.3× 3.0k 1.4× 1.5k 1.0× 1.1k 1.0× 2.8k 2.8× 271 6.2k
Bruce H. Krogh 1.8k 0.8× 2.8k 1.3× 3.0k 2.0× 748 0.7× 208 0.2× 228 8.0k
Sanjit A. Seshia 901 0.4× 1.2k 0.6× 2.0k 1.4× 2.8k 2.5× 495 0.5× 195 7.2k
Krishna R. Pattipati 2.6k 1.1× 3.4k 1.6× 259 0.2× 2.5k 2.2× 1.4k 1.4× 453 9.0k
Xenofon Koutsoukos 1.0k 0.4× 1.9k 0.9× 660 0.4× 1.2k 1.0× 164 0.2× 291 5.4k
Aaron D. Ames 429 0.2× 4.7k 2.2× 1.1k 0.7× 825 0.7× 852 0.9× 326 8.8k
Raymond A. DeCarlo 1.0k 0.4× 4.9k 2.3× 578 0.4× 275 0.2× 506 0.5× 131 6.3k
Feng Lin 1.1k 0.5× 1.8k 0.9× 4.2k 2.8× 781 0.7× 291 0.3× 334 7.0k
Rong Su 637 0.3× 1.6k 0.8× 1.6k 1.1× 738 0.6× 346 0.3× 332 4.5k

Countries citing papers authored by Ufuk Topcu

Since Specialization
Citations

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

Fields of papers citing papers by Ufuk Topcu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ufuk Topcu. 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 Ufuk Topcu. The network helps show where Ufuk Topcu may publish in the future.

Co-authorship network of co-authors of Ufuk Topcu

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

All Works

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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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