Nicolas Macris

77 papers receiving 863 citations

Peers

Nicolas Macris
Comparison fields: 5 of 67
  • Computational Mathematics 20
  • Condensed Matter Physics 184
  • Statistics and Probability 122
  • Computer Networks and Communications 276
  • Mathematical Physics 88
Replace Guilhem Semerjian with:
Guilhem Semerjian France
Masayuki Ohzeki Japan
Yi-Kai Liu United States
Dhagash Mehta United States
Shlomo Hoory Israel
Audrey Terras United States
Vyacheslav L. Girko Ukraine
Gérard Viennot France
J. C. A. Barata Brazil
Florent Benaych-Georges France
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Citations per field
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Citations per year

Countries citing papers authored by Nicolas Macris

Since Specialization
Citations

This map shows the geographic impact of Nicolas Macris'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 Nicolas Macris with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Macris more than expected).

Fields of papers citing papers by Nicolas Macris

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicolas Macris. 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 Nicolas Macris. The network helps show where Nicolas Macris may publish in the future.

Co-authors

The 25 scholars most cited alongside Nicolas Macris, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Nicolas Macris Line = papers co-authored together Nicolas Macris links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 79 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201997
2 201977
3 201970
4 201450
5 199935
6 199731
7 200727
8
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
201626
9 200925
10 201025
11
CHARACTERIZATION OF THE SPECTRUM OF THE LANDAU HAMILTONIAN WITH DELTA IMPURITIES
199921
12 201220
13 199920
14 201519
15 202117
16 199417
17 202216
18 201216
19 199316
20 200715

About Nicolas Macris

Nicolas Macris is a scholar working on Computational Mathematics, Condensed Matter Physics, Computer Networks and Communications, Statistics and Probability and Acoustics and Ultrasonics, having authored 79 papers that have together received 925 indexed citations. Recurring topics across this work include Error Correcting Code Techniques (26 papers), Theoretical and Computational Physics (15 papers), Advanced Wireless Communication Techniques (14 papers), Quantum and electron transport phenomena (12 papers), Cellular Automata and Applications (12 papers), Physics of Superconductivity and Magnetism (10 papers), Sparse and Compressive Sensing Techniques (8 papers) and Random Matrices and Applications (8 papers). The work is most often cited by research in Computational Mathematics (20 citations), Condensed Matter Physics (184 citations), Statistics and Probability (122 citations), Computer Networks and Communications (276 citations) and Mathematical Physics (88 citations). Nicolas Macris has collaborated with scholars based in Switzerland, France and United States. Frequent co-authors include Jean Barbier, Satish Babu Korada, Lenka Zdeborová, Florent Krząkała, J. V. Pulè, Léo Miolane, Shrinivas Kudekar, C. Gruber, Rüdiger Urbanke and J. K. Freericks. Their work appears in journals such as IEEE Transactions on Information Theory, Journal of Statistical Physics, Physical review. B, Condensed matter, Advanced Quantum Technologies and Letters in Mathematical Physics.

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.

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