Mathieu Riou
Impact in
- Artificial Intelligence top 2%
- Neural Networks and Reservoir Computing
- Neural Networks and Applications
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- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- Optical Network Technologies
Papers in
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- Neural Networks and Reservoir Computing 6
- Neural Networks and Applications 2
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- Advanced Memory and Neural Computing 6
- Ferroelectric and Negative Capacitance Devices 2
- Co-authors
- Flavio Abreu Araujo (6 shared papers)Damien Querlioz (5 shared papers)Jacob Torrejón (6 shared papers)Julie Grollier (6 shared papers)M. D. Stiles (4 shared papers)Hitoshi Kubota (5 shared papers)Sumito Tsunegi (5 shared papers)Shinji Yuasa (5 shared papers)
- Journals
- Physical Review Applied (1 paper)Nature Communications (1 paper)Ocean Engineering (1 paper)Industrial & Engineering Chemistry Research (1 paper)Nature (1 paper)
- Partner nations
- FranceJapanUnited States
In The Last Decade
Mathieu Riou
6 papers receiving 1.1k citations
Mathieu Riou's Hit Papers
Peers
Comparison fields: 5 of 56
- Artificial Intelligence 595
- Electrical and Electronic Engineering 837
- Atomic and Molecular Physics, and Optics 378
- Cognitive Neuroscience 186
- Condensed Matter Physics 96
Countries citing papers authored by Mathieu Riou
This map shows the geographic impact of Mathieu Riou'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 Mathieu Riou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathieu Riou more than expected).
Fields of papers citing papers by Mathieu Riou
This network shows the impact of papers produced by Mathieu Riou. 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 Mathieu Riou. The network helps show where Mathieu Riou may publish in the future.
Co-authors
The 19 scholars most cited alongside Mathieu Riou, 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 | Neuromorphic computing with nanoscale spintronic oscillators Hit paper breakdown → | 2017 | 945 |
| 2 | 2020 | 62 | |
| 3 | 2019 | 47 | |
| 4 | 2022 | 31 | |
| 5 | 2017 | 9 | |
| 6 | 2018 | 1 | |
| 7 | 2025 | 0 | |
| 8 | 2024 | 0 |
About Mathieu Riou
Mathieu Riou is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Computer Networks and Communications, Mechanics of Materials and Atomic and Molecular Physics, and Optics, having authored 8 papers that have together received 1.1k indexed citations. Recurring topics across this work include Neural Networks and Reservoir Computing (6 papers), Advanced Memory and Neural Computing (6 papers), Neural Networks and Applications (2 papers), Ferroelectric and Negative Capacitance Devices (2 papers), Fluid Dynamics Simulations and Interactions (1 paper), Magnetic properties of thin films (1 paper), Aerospace Engineering and Energy Systems (1 paper) and Cavitation Phenomena in Pumps (1 paper). The work is most often cited by research in Artificial Intelligence (595 citations), Electrical and Electronic Engineering (837 citations), Atomic and Molecular Physics, and Optics (378 citations), Cognitive Neuroscience (186 citations) and Condensed Matter Physics (96 citations). Mathieu Riou has collaborated with scholars based in France, Japan and United States. Frequent co-authors include Flavio Abreu Araujo, Damien Querlioz, Jacob Torrejón, Julie Grollier, M. D. Stiles, Hitoshi Kubota, Sumito Tsunegi, Shinji Yuasa, Kay Yakushiji and Akio Fukushima. Their work appears in journals such as Physical Review Applied, Nature Communications, Ocean Engineering, Industrial & Engineering Chemistry Research and Nature.
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.