Chen Tessler
Impact in
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Domain Adaptation and Few-Shot Learning
- Artificial Intelligence in Games
- Control and Systems Engineering top 10%
- Human Motion and Animation
- Robot Manipulation and Learning
Papers in
-
- Human Motion and Animation 4
-
- Network Traffic and Congestion Control 3
- Co-authors
- Shie Mannor (7 shared papers)Tom Zahavy (2 shared papers)Daniel J. Mankowitz (1 shared paper)Gal Chechik (6 shared papers)Xue Bin Peng (3 shared papers)Yunrong Guo (2 shared papers)Yoni Kasten (1 shared paper)Gal Dalal (4 shared papers)
- Journals
- ACM SIGMETRICS Performance Evaluation Review (1 paper)Machine Learning (1 paper)ACM Transactions on Graphics (1 paper)Computer Graphics Forum (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- IsraelUnited StatesUnited Kingdom
In The Last Decade
Chen Tessler
10 papers receiving 294 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 185
- Control and Systems Engineering 61
- Computer Networks and Communications 29
- Computer Vision and Pattern Recognition 19
- Automotive Engineering 7
Countries citing papers authored by Chen Tessler
This map shows the geographic impact of Chen Tessler'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 Chen Tessler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chen Tessler more than expected).
Fields of papers citing papers by Chen Tessler
This network shows the impact of papers produced by Chen Tessler. 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 Chen Tessler. The network helps show where Chen Tessler may publish in the future.
Co-authors
The 19 scholars most cited alongside Chen Tessler, 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 | 2017 | 187 | |
| 2 | 2023 | 45 | |
| 3 | 2022 | 20 | |
| 4 | 2024 | 20 | |
| 5 | 2021 | 15 | |
| 6 | 2022 | 5 | |
| 7 | 2023 | 4 | |
| 8 | 2022 | 3 | |
| 9 | 2024 | 3 | |
| 10 | 2025 | 1 | |
| 11 | 2025 | 0 |
About Chen Tessler
Chen Tessler is a scholar working on Control and Systems Engineering, Computer Networks and Communications, Artificial Intelligence, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 11 papers that have together received 303 indexed citations. Recurring topics across this work include Human Motion and Animation (4 papers), Reinforcement Learning in Robotics (3 papers), Network Traffic and Congestion Control (3 papers) and Artificial Intelligence in Games (1 paper). The work is most often cited by research in Artificial Intelligence (185 citations), Control and Systems Engineering (61 citations), Computer Networks and Communications (29 citations), Computer Vision and Pattern Recognition (19 citations) and Automotive Engineering (7 citations). Chen Tessler has collaborated with scholars based in Israel, United States and United Kingdom. Frequent co-authors include Shie Mannor, Tom Zahavy, Daniel J. Mankowitz, Gal Chechik, Xue Bin Peng, Yunrong Guo, Yoni Kasten, Gal Dalal, Benjamin Fuhrer and Dotan Di Castro. Their work appears in journals such as ACM SIGMETRICS Performance Evaluation Review, Machine Learning, ACM Transactions on Graphics, Computer Graphics Forum and Lecture notes in computer science.
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.