Serkan Cabi
Impact in
- Astronomy and Astrophysics top 10%
- Cosmology and Gravitation Theories
- Advanced Differential Geometry Research
- Control and Systems Engineering top 10%
- Robot Manipulation and Learning
Papers in
-
- Protein Degradation and Inhibitors 2
- Ubiquitin and proteasome pathways 1
-
- Advanced Vision and Imaging 1
- Context-Aware Activity Recognition Systems 1
- Co-authors
- Yi Mao (1 shared paper)Max Tegmark (1 shared paper)Alan H. Guth (1 shared paper)Yuke Zhu (1 shared paper)Nando de Freitas (3 shared papers)Andrei A. Rusu (1 shared paper)Tom Erez (1 shared paper)Nicolas Heess (1 shared paper)
- Journals
- Cell Metabolism (2 papers)Nature Nanotechnology (1 paper)arXiv (Cornell University) (1 paper)Physical review. D. Particles, fields, gravitation, and cosmology (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Serkan Cabi
7 papers receiving 283 citations
Peers
Comparison fields: 5 of 68
- Astronomy and Astrophysics 62
- Control and Systems Engineering 86
- Nuclear and High Energy Physics 38
- Artificial Intelligence 81
- Computer Vision and Pattern Recognition 42
Countries citing papers authored by Serkan Cabi
This map shows the geographic impact of Serkan Cabi'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 Serkan Cabi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Serkan Cabi more than expected).
Fields of papers citing papers by Serkan Cabi
This network shows the impact of papers produced by Serkan Cabi. 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 Serkan Cabi. The network helps show where Serkan Cabi may publish in the future.
Co-authors
The 25 scholars most cited alongside Serkan Cabi, 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 | 2018 | 130 | |
| 2 | 2007 | 65 | |
| 3 | 2014 | 56 | |
| 4 | 2021 | 29 | |
| 5 | A Framework for Data-Driven Robotics | 2019 | 4 |
| 6 | The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously | 2017 | 1 |
| 7 | 2014 | 1 |
About Serkan Cabi
Serkan Cabi is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition, Artificial Intelligence, Cell Biology and Astronomy and Astrophysics, having authored 7 papers that have together received 286 indexed citations. Recurring topics across this work include Endoplasmic Reticulum Stress and Disease (2 papers), Protein Degradation and Inhibitors (2 papers), Relativity and Gravitational Theory (1 paper), Ubiquitin and proteasome pathways (1 paper), Advanced Vision and Imaging (1 paper), Context-Aware Activity Recognition Systems (1 paper), Reinforcement Learning in Robotics (1 paper) and Force Microscopy Techniques and Applications (1 paper). The work is most often cited by research in Astronomy and Astrophysics (62 citations), Control and Systems Engineering (86 citations), Nuclear and High Energy Physics (38 citations), Artificial Intelligence (81 citations) and Computer Vision and Pattern Recognition (42 citations). Serkan Cabi has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Yi Mao, Max Tegmark, Alan H. Guth, Yuke Zhu, Nando de Freitas, Andrei A. Rusu, Tom Erez, Nicolas Heess, Josh Merel and Saran Tunyasuvunakool. Their work appears in journals such as Cell Metabolism, Nature Nanotechnology, arXiv (Cornell University) and Physical review. D. Particles, fields, gravitation, and cosmology.
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.