Amir‐massoud Farahmand
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Media Technology top 10%
- Computational Theory and Mathematics
- Co-authors
- Azad ShademanMartin JägersandCsaba SzepesváriMouhacine BenosmanDaniel NikovskiSaleh NabiMohammad GhavamzadehShie Mannor
- Topics
- Reinforcement Learning in Robotics (10 papers)Advanced Control Systems Optimization (6 papers)Control Systems and Identification (5 papers)
- Partner nations
- United StatesCanadaFrance
In The Last Decade
Amir‐massoud Farahmand
22 papers receiving 192 citations
Peers
Comparison fields: 5 of 59
- Artificial Intelligence 84
- Computer Vision and Pattern Recognition 55
- Control and Systems Engineering 52
- Media Technology 31
- Computational Theory and Mathematics 29
Countries citing papers authored by Amir‐massoud Farahmand
This map shows the geographic impact of Amir‐massoud Farahmand'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 Amir‐massoud Farahmand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir‐massoud Farahmand more than expected).
Fields of papers citing papers by Amir‐massoud Farahmand
This network shows the impact of papers produced by Amir‐massoud Farahmand. 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 Amir‐massoud Farahmand. The network helps show where Amir‐massoud Farahmand may publish in the future.
Co-authorship network of co-authors of Amir‐massoud Farahmand
This figure shows the co-authorship network connecting the top 25 collaborators of Amir‐massoud Farahmand. A scholar is included among the top collaborators of Amir‐massoud Farahmand 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 Amir‐massoud Farahmand. Amir‐massoud Farahmand is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | PID Accelerated Value Iteration Algorithm | 1 |
| 2 | Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm | 2 |
| 3 | 9 | |
| 4 | Iterative Value-Aware Model Learning | 5 |
| 5 | Dimensionality Reduction for Representing the Knowledge of Probabilistic Models | 3 |
| 6 | Random Projection Filter Bank for Time Series Data. | 1 |
| 7 | 22 | |
| 8 | 1 | |
| 9 | Regularized policy iteration with nonparametric function spaces | 19 |
| 10 | 0 | |
| 11 | 8 | |
| 12 | 4 | |
| 13 | 11 | |
| 14 | 3 | |
| 15 | Sample-based approximate regularization | 1 |
| 16 | Generalized Classication-bas ed Approximate Policy Iteration | 3 |
| 17 | Action-Gap Phenomenon in Reinforcement Learning | 16 |
| 18 | 20 | |
| 19 | 52 | |
| 20 | 9 |
About Amir‐massoud Farahmand
Amir‐massoud Farahmand is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computational Theory and Mathematics, having authored 23 papers that have together received 204 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Advanced Control Systems Optimization (6 papers) and Control Systems and Identification (5 papers). The work is most often cited by research in Media Technology (31 citations), Computer Vision and Pattern Recognition (55 citations) and Artificial Intelligence (84 citations). Amir‐massoud Farahmand has collaborated with scholars based in United States, Canada and France. Frequent co-authors include Azad Shademan, Martin Jägersand, Csaba Szepesvári, Mouhacine Benosman, Daniel Nikovski, Saleh Nabi, Mohammad Ghavamzadeh, Shie Mannor, Meng Xia and De-An Huang. Their work appears in journals such as IEEE Transactions on Automatic Control, Machine Learning and Journal of Machine Learning Research.
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.