Dotan Di Castro
- Electrical and Electronic Engineering
- Artificial Intelligence top 10%
- Cellular and Molecular Neuroscience
- Control and Systems Engineering
- Information Systems top 10%
- Co-authors
- Daniel SoudryShahar KvatinskyA. GalAvinoam KolodnyLiane Lewin-EytanShie MannorRon MeirAviv Tamar
- Topics
- Reinforcement Learning in Robotics (8 papers)Robot Manipulation and Learning (6 papers)Neural dynamics and brain function (3 papers)
- Cited by
- Information Systems and ManagementArtificial IntelligenceCellular and Molecular Neuroscience
- Partner nations
- IsraelUnited StatesIndia
In The Last Decade
Dotan Di Castro
23 papers receiving 382 citations
Peers
Comparison fields: 5 of 57
- Electrical and Electronic Engineering 191
- Artificial Intelligence 138
- Cellular and Molecular Neuroscience 76
- Control and Systems Engineering 59
- Information Systems 49
Countries citing papers authored by Dotan Di Castro
This map shows the geographic impact of Dotan Di Castro'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 Dotan Di Castro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dotan Di Castro more than expected).
Fields of papers citing papers by Dotan Di Castro
This network shows the impact of papers produced by Dotan Di Castro. 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 Dotan Di Castro. The network helps show where Dotan Di Castro may publish in the future.
Co-authorship network of co-authors of Dotan Di Castro
This figure shows the co-authorship network connecting the top 25 collaborators of Dotan Di Castro. A scholar is included among the top collaborators of Dotan Di Castro 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 Dotan Di Castro. Dotan Di Castro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 14 | |
| 9 | 6 | |
| 10 | 7 | |
| 11 | 29 | |
| 12 | 20 | |
| 13 | 28 | |
| 14 | 211 | |
| 15 | Temporal Difference Methods for the Variance of the Reward To Go | 14 |
| 16 | 0 | |
| 17 | Integrating Partial Model Knowledge in Model Free RL Algorithms | 1 |
| 18 | 15 | |
| 19 | Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation | 11 |
| 20 | 0 |
About Dotan Di Castro
Dotan Di Castro is a scholar working on Human-Computer Interaction, Artificial Intelligence and Control and Systems Engineering, having authored 27 papers that have together received 404 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Robot Manipulation and Learning (6 papers) and Neural dynamics and brain function (3 papers). The work is most often cited by research in Information Systems and Management (40 citations), Artificial Intelligence (138 citations) and Cellular and Molecular Neuroscience (76 citations). Dotan Di Castro has collaborated with scholars based in Israel, United States and India. Frequent co-authors include Daniel Soudry, Shahar Kvatinsky, A. Gal, Avinoam Kolodny, Liane Lewin-Eytan, Shie Mannor, Ron Meir, Aviv Tamar, Yoelle Maarek and Zohar Karnin. Their work appears in journals such as IEEE Access, Neural Computation and IEEE Transactions on Neural Networks and Learning Systems.
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.