Léo Miolane
Impact in
- Computational Mathematics top 5%
- Tensor decomposition and applications
- Statistics and Probability top 5%
- Random Matrices and Applications
- Statistical Methods and Inference
- Markov Chains and Monte Carlo Methods
Papers in
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- Sparse and Compressive Sensing Techniques 5
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- Random Matrices and Applications 4
- Markov Chains and Monte Carlo Methods 1
- Co-authors
- Marc Lelarge (2 shared papers)Florent Krząkała (3 shared papers)Jean Barbier (3 shared papers)Nicolas Macris (3 shared papers)Lenka Zdeborová (3 shared papers)Andrea Montanari (1 shared paper)Francesco Caltagirone (1 shared paper)Aukosh Jagannath (1 shared paper)
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Probability Theory and Related Fields (1 paper)IEEE Transactions on Network Science and Engineering (1 paper)The Annals of Applied Probability (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- FranceSwitzerlandIndia
In The Last Decade
Léo Miolane
7 papers receiving 246 citations
Peers
Comparison fields: 5 of 43
- Computational Mathematics 19
- Statistics and Probability 120
- Acoustics and Ultrasonics 5
- Signal Processing 46
- Computational Mechanics 74
Countries citing papers authored by Léo Miolane
This map shows the geographic impact of Léo Miolane'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 Léo Miolane with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Léo Miolane more than expected).
Fields of papers citing papers by Léo Miolane
This network shows the impact of papers produced by Léo Miolane. 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 Léo Miolane. The network helps show where Léo Miolane may publish in the future.
Co-authors
The 8 scholars most cited alongside Léo Miolane, 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 | 2019 | 100 | |
| 2 | 2018 | 72 | |
| 3 | 2018 | 40 | |
| 4 | 2020 | 13 | |
| 5 | 2017 | 12 | |
| 6 | Phase Transitions, Optimal Errors and Optimality of Message-Passing in Generalized Linear Models | 2017 | 7 |
| 7 | 2017 | 4 | |
| 8 | Fundamental limits of low-rank matrix estimation | 2017 | 0 |
About Léo Miolane
Léo Miolane is a scholar working on Computational Mechanics, Statistics and Probability, Signal Processing, Artificial Intelligence and Control and Systems Engineering, having authored 8 papers that have together received 248 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (5 papers), Blind Source Separation Techniques (4 papers), Random Matrices and Applications (4 papers), Neural Networks and Applications (3 papers), Stochastic processes and financial applications (1 paper), Tensor decomposition and applications (1 paper), Complex Network Analysis Techniques (1 paper) and Markov Chains and Monte Carlo Methods (1 paper). The work is most often cited by research in Computational Mathematics (19 citations), Statistics and Probability (120 citations), Acoustics and Ultrasonics (5 citations), Signal Processing (46 citations) and Computational Mechanics (74 citations). Léo Miolane has collaborated with scholars based in France, Switzerland and India. Frequent co-authors include Marc Lelarge, Florent Krząkała, Jean Barbier, Nicolas Macris, Lenka Zdeborová, Andrea Montanari, Francesco Caltagirone and Aukosh Jagannath. Their work appears in journals such as Proceedings of the National Academy of Sciences, Probability Theory and Related Fields, IEEE Transactions on Network Science and Engineering, The Annals of Applied Probability 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.