Davood Hajinezhad
- Artificial Intelligence top 5%
- Computer Networks and Communications top 5%
- Computational Mechanics top 5%
- Computational Theory and Mathematics top 5%
- Control and Systems Engineering top 10%
- Co-authors
- Afshin OroojlooyMingyi HongQingjiang ShiAlfredo GarcíaTsung‐Hui ChangXiangfeng WangMichael M. ZavlanosTuo Zhao
- Topics
- Sparse and Compressive Sensing Techniques (8 papers)Stochastic Gradient Optimization Techniques (3 papers)Distributed Control Multi-Agent Systems (3 papers)
- Partner nations
- United StatesChinaPortugal
In The Last Decade
Davood Hajinezhad
12 papers receiving 546 citations
Hit Papers
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 240
- Computer Networks and Communications 205
- Computational Mechanics 159
- Computational Theory and Mathematics 86
- Control and Systems Engineering 81
Countries citing papers authored by Davood Hajinezhad
This map shows the geographic impact of Davood Hajinezhad'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 Davood Hajinezhad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Davood Hajinezhad more than expected).
Fields of papers citing papers by Davood Hajinezhad
This network shows the impact of papers produced by Davood Hajinezhad. 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 Davood Hajinezhad. The network helps show where Davood Hajinezhad may publish in the future.
Co-authorship network of co-authors of Davood Hajinezhad
This figure shows the co-authorship network connecting the top 25 collaborators of Davood Hajinezhad. A scholar is included among the top collaborators of Davood Hajinezhad 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 Davood Hajinezhad. Davood Hajinezhad is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | A review of cooperative multi-agent deep reinforcement learningbreakdown → | 217 |
| 3 | 51 | |
| 4 | 22 | |
| 5 | 36 | |
| 6 | 59 | |
| 7 | 5 | |
| 8 | 25 | |
| 9 | 69 | |
| 10 | NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization | 14 |
| 11 | 44 | |
| 12 | 22 |
About Davood Hajinezhad
Davood Hajinezhad is a scholar working on Computational Mechanics, Signal Processing and Computational Theory and Mathematics, having authored 12 papers that have together received 565 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (8 papers), Stochastic Gradient Optimization Techniques (3 papers) and Distributed Control Multi-Agent Systems (3 papers). The work is most often cited by research in Computer Networks and Communications (205 citations), Computational Mathematics (5 citations) and Numerical Analysis (42 citations). Davood Hajinezhad has collaborated with scholars based in United States, China and Portugal. Frequent co-authors include Afshin Oroojlooy, Mingyi Hong, Qingjiang Shi, Alfredo García, Tsung‐Hui Chang, Xiangfeng Wang, Michael M. Zavlanos, Tuo Zhao, Zhaoran Wang and Yan Zhang. Their work appears in journals such as IEEE Transactions on Automatic Control, Mathematical Programming and Applied Intelligence.
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.