Katharina Mülling
- Control and Systems Engineering top 2%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering
- Mechanical Engineering
- Co-authors
- Jan PetersJens KoberYasemin AltünOliver KroemerBernhard SchölkopfZhikun WangHeni Ben AmorMarc Peter Deisenroth
- Topics
- Reinforcement Learning in Robotics (7 papers)Robot Manipulation and Learning (6 papers)Motor Control and Adaptation (3 papers)
- Cited by
- Control and Systems EngineeringArtificial IntelligenceComputer Vision and Pattern Recognition
- Partner nations
- GermanyUnited StatesAustria
In The Last Decade
Katharina Mülling
12 papers receiving 837 citations
Peers
Comparison fields: 5 of 62
- Control and Systems Engineering 526
- Artificial Intelligence 486
- Computer Vision and Pattern Recognition 219
- Biomedical Engineering 191
- Mechanical Engineering 80
Countries citing papers authored by Katharina Mülling
This map shows the geographic impact of Katharina Mülling'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 Katharina Mülling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katharina Mülling more than expected).
Fields of papers citing papers by Katharina Mülling
This network shows the impact of papers produced by Katharina Mülling. 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 Katharina Mülling. The network helps show where Katharina Mülling may publish in the future.
Co-authorship network of co-authors of Katharina Mülling
This figure shows the co-authorship network connecting the top 25 collaborators of Katharina Mülling. A scholar is included among the top collaborators of Katharina Mülling 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 Katharina Mülling. Katharina Mülling is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 30 | |
| 3 | Seminar in Artificial Intelligence | 0 |
| 4 | 21 | |
| 5 | Inverse Reinforcement Learning for Strategy Extraction | 2 |
| 6 | 110 | |
| 7 | 250 | |
| 8 | 65 | |
| 9 | 11 | |
| 10 | 1 | |
| 11 | 9 | |
| 12 | 259 | |
| 13 | 108 |
About Katharina Mülling
Katharina Mülling is a scholar working on Artificial Intelligence, Control and Systems Engineering and Cognitive Neuroscience, having authored 13 papers that have together received 869 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Robot Manipulation and Learning (6 papers) and Motor Control and Adaptation (3 papers). The work is most often cited by research in Control and Systems Engineering (526 citations), Artificial Intelligence (486 citations) and Computer Vision and Pattern Recognition (219 citations). Katharina Mülling has collaborated with scholars based in Germany, United States and Austria. Frequent co-authors include Jan Peters, Jens Kober, Yasemin Altün, Oliver Kroemer, Bernhard Schölkopf, Zhikun Wang, Heni Ben Amor, Marc Peter Deisenroth, David Vogt and Abdeslam Boularias. Their work appears in journals such as Artificial Intelligence, The International Journal of Robotics Research and Adaptive Behavior.
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.