Yaakov Engel
- Signal Processing top 2%
- Artificial Intelligence top 2%
- Reinforcement Learning in Robotics 4
- Gaussian Processes and Bayesian Inference 3
- Bayesian Modeling and Causal Inference 2
- Computational Mechanics top 2%
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- Advanced Chemical Physics Studies 8
- Spectroscopy and Quantum Chemical Studies 6
- Quantum, superfluid, helium dynamics 2
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- Quantum chaos and dynamical systems 7
- Statistical Mechanics and Entropy 3
Yaakov Engel
30 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 103
- Signal Processing 359
- Nuclear and High Energy Physics 270
- Artificial Intelligence 661
- Computational Mechanics 417
- Control and Systems Engineering 348
Countries citing papers authored by Yaakov Engel
This map shows the geographic impact of Yaakov Engel'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 Yaakov Engel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yaakov Engel more than expected).
Fields of papers citing papers by Yaakov Engel
This network shows the impact of papers produced by Yaakov Engel. 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 Yaakov Engel. The network helps show where Yaakov Engel may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yaakov Engel, 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 | 2010 | 11 | |
| 2 | 2008 | 10 | |
| 3 | 2007 | 32 | |
| 4 | Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods | 2005 | 37 |
| 5 | 2005 | 186 | |
| 6 | 2005 | 189 | |
| 7 | Algorithms and Representations for Reinforcement Learning | 2005 | 39 |
| 8 | Bayes meets bellman: the Gaussian process approach to temporal difference learning | 2003 | 101 |
| 9 | Sparse Online Greedy Support Vector Regression | 2002 | 6 |
| 10 | Learning Embedded Maps of Markov Processes | 2001 | 4 |
| 11 | 1997 | 4 | |
| 12 | 1990 | 51 | |
| 13 | 1988 | 44 | |
| 14 | 1988 | 18 | |
| 15 | 1988 | 8 | |
| 16 | 1987 | 21 | |
| 17 | 1987 | 19 | |
| 18 | 1981 | 34 | |
| 19 | 1977 | 1 | |
| 20 | 1975 | 315 |
About Yaakov Engel
Yaakov Engel is a scholar working on Statistical and Nonlinear Physics, Atomic and Molecular Physics, and Optics and Spectroscopy, having authored 31 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Chemical Physics Studies (8 papers), Quantum chaos and dynamical systems (7 papers), Spectroscopy and Quantum Chemical Studies (6 papers), Reinforcement Learning in Robotics (4 papers), Statistical Mechanics and Entropy (3 papers), Gaussian Processes and Bayesian Inference (3 papers), Bayesian Modeling and Causal Inference (2 papers) and Quantum, superfluid, helium dynamics (2 papers). The work is most often cited by research in Signal Processing (359 citations), Nuclear and High Energy Physics (270 citations) and Artificial Intelligence (661 citations). Yaakov Engel has collaborated with scholars based in Israel, United States and Canada. Frequent co-authors include Shie Mannor, Ron Meir, R. D. Levine, K. Goeke, S. J. Krieger, D. Vautherin, D.M. Brink, Binyamin Hochner, Ranit Aharonov and Tamar Flash. Their work appears in journals such as The Journal of Chemical Physics, Chemical Physics Letters, Israel Journal of Chemistry, Journal of Artificial Intelligence Research and Chemical Physics.
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.