Mojmír Mutný

526 citations
12 papers · 180 · h-index 6

Impact in

Papers in

Mojmír Mutný

12 papers receiving 177 citations

Peers

Mojmír Mutný
Comparison fields: 5 of 57
  • Biomedical Engineering 79
  • Control and Systems Engineering 42
  • Management Science and Operations Research 16
  • Computational Theory and Mathematics 20
  • Artificial Intelligence 40
Replace Arjuna Marzuki with:
Arjuna Marzuki Malaysia
D. Stark United States
P.G. Drennan United States
Jai Narayan Tripathi India
Nikola Djurić Serbia
Fábio Passos Spain
Onur Soysal United States
Siqi Wang France
sarah‐marie belcastro United States
Mojmír Mutný relative to Arjuna Marzuki Malaysia Arjuna Marzuki's profile →
Citations per field
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Arjuna Marzuki · 1×
Citations per year

Countries citing papers authored by Mojmír Mutný

Since Specialization
Citations

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

Fields of papers citing papers by Mojmír Mutný

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mojmír Mutný, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mojmír Mutný Line = papers co-authored together Mojmír Mutný links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 201987
2 201931
3 202417
4 201916
5 202411
6 20197
7 20253
8 20242
9
No-regret Algorithms for Capturing Events in Poisson Point Processes
20212
10
Coresets via Bilevel Optimization for Continual Learning and Streaming
20202
11
Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
20211
12 20201

About Mojmír Mutný

Mojmír Mutný is a scholar working on Management Science and Operations Research, Molecular Biology, Artificial Intelligence, Signal Processing and Computational Theory and Mathematics, having authored 12 papers that have together received 180 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (4 papers), Protein Structure and Dynamics (2 papers), Gaussian Processes and Bayesian Inference (2 papers), RNA and protein synthesis mechanisms (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Multimodal Machine Learning Applications (1 paper), Soft Robotics and Applications (1 paper) and Machine Learning in Materials Science (1 paper). The work is most often cited by research in Biomedical Engineering (79 citations), Control and Systems Engineering (42 citations), Management Science and Operations Research (16 citations), Computational Theory and Mathematics (20 citations) and Artificial Intelligence (40 citations). Mojmír Mutný has collaborated with scholars based in Switzerland, Germany and Spain. Frequent co-authors include Andreas Krause, Espen Knoop, Moritz Bächer, Miguel Á. Otaduy, R. Ischebeck, Nicole Hiller, Thomas R. Ward, Tobias Vornholt, Christian Schellhaas and Sven Panke. Their work appears in journals such as ACS Central Science, Nature Chemical Biology, Soft Robotics, DORA PSI (Paul Scherrer Institute) and Proceedings of the AAAI Conference on 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.

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