Gal Dalal
- Artificial Intelligence
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Safety, Risk, Reliability and Quality top 10%
- Management Science and Operations Research
- Co-authors
- Shie MannorBalázs SzörényiElad GilboaLouis WehenkelOfer LaviBruno ScherrerBenjamin FuhrerChen Tessler
- Topics
- Reinforcement Learning in Robotics (6 papers)Optimal Power Flow Distribution (4 papers)Evolutionary Algorithms and Applications (4 papers)
- Cited by
- Safety, Risk, Reliability and QualityArtificial IntelligenceManagement Science and Operations Research
- Journals
- IEEE Transactions on Power SystemsACM SIGMETRICS Performance Evaluation ReviewarXiv (Cornell University)
- Partner nations
- IsraelUnited KingdomUnited States
In The Last Decade
Gal Dalal
13 papers receiving 193 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 87
- Electrical and Electronic Engineering 78
- Computer Networks and Communications 54
- Safety, Risk, Reliability and Quality 39
- Management Science and Operations Research 32
Countries citing papers authored by Gal Dalal
This map shows the geographic impact of Gal Dalal'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 Gal Dalal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gal Dalal more than expected).
Fields of papers citing papers by Gal Dalal
This network shows the impact of papers produced by Gal Dalal. 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 Gal Dalal. The network helps show where Gal Dalal may publish in the future.
Co-authorship network of co-authors of Gal Dalal
This figure shows the co-authorship network connecting the top 25 collaborators of Gal Dalal. A scholar is included among the top collaborators of Gal Dalal 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 Gal Dalal. Gal Dalal 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 | 17 | |
| 3 | 1 | |
| 4 | 14 | |
| 5 | 5 | |
| 6 | 27 | |
| 7 | Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning | 5 |
| 8 | 41 | |
| 9 | 17 | |
| 10 | Concentration Bounds for Two Timescale Stochastic Approximation with Applications to Reinforcement Learning | 1 |
| 11 | 24 | |
| 12 | 18 | |
| 13 | 27 | |
| 14 | Hierarchical decision making in electricity grid management | 6 |
About Gal Dalal
Gal Dalal is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Networks and Communications, having authored 14 papers that have together received 203 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Optimal Power Flow Distribution (4 papers) and Evolutionary Algorithms and Applications (4 papers). The work is most often cited by research in Safety, Risk, Reliability and Quality (39 citations), Artificial Intelligence (87 citations) and Management Science and Operations Research (32 citations). Gal Dalal has collaborated with scholars based in Israel, United Kingdom and United States. Frequent co-authors include Shie Mannor, Balázs Szörényi, Elad Gilboa, Louis Wehenkel, Ofer Lavi, Bruno Scherrer, Benjamin Fuhrer, Chen Tessler and Gal Chechik. Their work appears in journals such as IEEE Transactions on Power Systems, ACM SIGMETRICS Performance Evaluation Review and arXiv (Cornell University).
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.