David Saad
Impact in
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- Complex Network Analysis Techniques
- Model Reduction and Neural Networks
- Artificial Intelligence top 2%
- Neural Networks and Applications
Papers in
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- Model Reduction and Neural Networks 26
- Complex Network Analysis Techniques 22
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- Neural Networks and Applications 63
- Machine Learning and ELM 15
- Co-authors
- Yoshiyuki Kabashima (23 shared papers)Sara A. Solla (5 shared papers)MTW (1 shared paper)Magnus Rattray (12 shared papers)Chi Ho Yeung (15 shared papers)Tatsuto Murayama (4 shared papers)Renato Vicente (8 shared papers)K. Y. Michael Wong (10 shared papers)
- Journals
- Physical Review Letters (12 papers)Europhysics Letters (EPL) (6 papers)Journal of Physics A Mathematical and Theoretical (5 papers)Physical review. E (5 papers)Neural Computation (4 papers)
- Partner nations
- United KingdomJapanHong Kong
In The Last Decade
David Saad
158 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 123
- Statistical and Nonlinear Physics 574
- Artificial Intelligence 1.1k
- Signal Processing 333
- Computer Networks and Communications 551
- Computational Theory and Mathematics 328
Countries citing papers authored by David Saad
This map shows the geographic impact of David Saad'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 David Saad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Saad more than expected).
Fields of papers citing papers by David Saad
This network shows the impact of papers produced by David Saad. 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 David Saad. The network helps show where David Saad may publish in the future.
Co-authors
The 25 scholars most cited alongside David Saad, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 166 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2000 | 195 | |
| 2 | 1995 | 138 | |
| 3 | 1995 | 116 | |
| 4 | 1999 | 111 | |
| 5 | 1999 | 91 | |
| 6 | 1998 | 86 | |
| 7 | 2016 | 77 | |
| 8 | 1998 | 67 | |
| 9 | 1989 | 61 | |
| 10 | 2017 | 59 | |
| 11 | 2000 | 58 | |
| 12 | 2000 | 56 | |
| 13 | 2004 | 54 | |
| 14 | 2013 | 47 | |
| 15 | 1999 | 42 | |
| 16 | 2013 | 36 | |
| 17 | 1995 | 34 | |
| 18 | 1997 | 33 | |
| 19 | 2005 | 33 | |
| 20 | 1997 | 32 |
About David Saad
David Saad is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Computer Networks and Communications, Signal Processing and Computational Theory and Mathematics, having authored 166 papers that have together received 2.4k indexed citations. Recurring topics across this work include Neural Networks and Applications (63 papers), Error Correcting Code Techniques (38 papers), Model Reduction and Neural Networks (26 papers), Complex Network Analysis Techniques (22 papers), Blind Source Separation Techniques (16 papers), Machine Learning and ELM (15 papers), Cellular Automata and Applications (15 papers) and Advanced Wireless Communication Techniques (15 papers). The work is most often cited by research in Statistical and Nonlinear Physics (574 citations), Artificial Intelligence (1.1k citations), Signal Processing (333 citations), Computer Networks and Communications (551 citations) and Computational Theory and Mathematics (328 citations). David Saad has collaborated with scholars based in United Kingdom, Japan and Hong Kong. Frequent co-authors include Yoshiyuki Kabashima, Sara A. Solla, MTW, Magnus Rattray, Chi Ho Yeung, Tatsuto Murayama, Renato Vicente, K. Y. Michael Wong, Andrey Y. Lokhov and Ido Kanter. Their work appears in journals such as Physical Review Letters, Europhysics Letters (EPL), Journal of Physics A Mathematical and Theoretical, Physical review. E and Neural Computation.
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.