Robert Pinsler
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
- Enzyme Structure and Function
Papers in
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- Machine Learning and Data Classification 1
- Machine Learning and Algorithms 1
- Reinforcement Learning in Robotics 1
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- Protein Structure and Dynamics 1
- Co-authors
- Marco Orsini Federici (1 shared paper)Victor García Satorras (1 shared paper)Chin‐Wei Huang (1 shared paper)Cecilia Clementi (1 shared paper)Frank Noé (1 shared paper)Rianne van den Berg (1 shared paper)Daniel Zügner (1 shared paper)Jonathan Gordon (1 shared paper)
- Journals
- Journal of Chemical Theory and Computation (1 paper)UvA-DARE (University of Amsterdam) (1 paper)Lincoln Repository (University of Lincoln) (1 paper)
- Partner nations
- United KingdomGermanyNetherlands
In The Last Decade
Robert Pinsler
3 papers receiving 66 citations
Peers
Comparison fields: 5 of 26
- Computational Theory and Mathematics 20
- Materials Chemistry 31
- Artificial Intelligence 19
- Molecular Biology 32
- Computer Vision and Pattern Recognition 8
Countries citing papers authored by Robert Pinsler
This map shows the geographic impact of Robert Pinsler'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 Robert Pinsler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Pinsler more than expected).
Fields of papers citing papers by Robert Pinsler
This network shows the impact of papers produced by Robert Pinsler. 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 Robert Pinsler. The network helps show where Robert Pinsler may publish in the future.
Co-authors
The 14 scholars most cited alongside Robert Pinsler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 48 | |
| 2 | Bayesian Batch Active Learning as Sparse Subset Approximation | 2019 | 12 |
| 3 | 2018 | 8 |
About Robert Pinsler
Robert Pinsler is a scholar working on Artificial Intelligence, Molecular Biology, Control and Systems Engineering, Statistical and Nonlinear Physics and Cognitive Neuroscience, having authored 3 papers that have together received 68 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (1 paper), Machine Learning and Algorithms (1 paper), Advanced Statistical Process Monitoring (1 paper), Reinforcement Learning in Robotics (1 paper), Protein Structure and Dynamics (1 paper), Motor Control and Adaptation (1 paper), Model Reduction and Neural Networks (1 paper) and Robot Manipulation and Learning (1 paper). The work is most often cited by research in Computational Theory and Mathematics (20 citations), Materials Chemistry (31 citations), Artificial Intelligence (19 citations), Molecular Biology (32 citations) and Computer Vision and Pattern Recognition (8 citations). Robert Pinsler has collaborated with scholars based in United Kingdom, Germany and Netherlands. Frequent co-authors include Marco Orsini Federici, Victor García Satorras, Chin‐Wei Huang, Cecilia Clementi, Frank Noé, Rianne van den Berg, Daniel Zügner, Jonathan Gordon, Eric Nalisnick and José Miguel Hernández-Lobato. Their work appears in journals such as Journal of Chemical Theory and Computation, UvA-DARE (University of Amsterdam) and Lincoln Repository (University of Lincoln).
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.