Jonas Kahn

1.2k citations
29 papers · 591 · h-index 14

Impact in

Papers in

Jonas Kahn

28 papers receiving 570 citations

Peers

Jonas Kahn
Comparison fields: 5 of 59
  • Computer Graphics and Computer-Aided Design 81
  • Artificial Intelligence 332
  • Atomic and Molecular Physics, and Optics 254
  • Statistics and Probability 47
  • Computational Mechanics 97
Replace Shayne Waldron with:
Shayne Waldron New Zealand
F. Silva Leite Portugal
Christoph Zenger Germany
Yuanzhe Xi United States
Takashi Komatsu Japan
Benedetta Morini Italy
Ajil Jalal United States
Henri-François Raynaud France
Peter J. Grabner Austria
Eli Passow United States
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Citations per field
00.5×4.6×
Shayne Waldron · 1×
Citations per year

Countries citing papers authored by Jonas Kahn

Since Specialization
Citations

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

Fields of papers citing papers by Jonas Kahn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Jonas Kahn, 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 Jonas Kahn Line = papers co-authored together Jonas Kahn links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 198395
2 200966
3 202060
4 200644
5 200536
6 201436
7 201630
8 201828
9 200726
10 200725
11 200521
12 202220
13 201619
14 201616
15 201813
16 201910
17 20078
18 20188
19 20086
20
Strong identifiability and optimal minimax rates for finite mixture estimation
20184

About Jonas Kahn

Jonas Kahn is a scholar working on Artificial Intelligence, Computational Mechanics, Mathematical Physics, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics, having authored 29 papers that have together received 591 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (10 papers), Quantum Computing Algorithms and Architecture (8 papers), Sparse and Compressive Sensing Techniques (7 papers), Quantum Mechanics and Applications (4 papers), Markov Chains and Monte Carlo Methods (4 papers), Spectral Theory in Mathematical Physics (3 papers), Topological and Geometric Data Analysis (3 papers) and Advanced MRI Techniques and Applications (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (81 citations), Artificial Intelligence (332 citations), Atomic and Molecular Physics, and Optics (254 citations), Statistics and Probability (47 citations) and Computational Mechanics (97 citations). Jonas Kahn has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include Mădălin Guţǎ, Daniel J. Kleitman, Maria Klawe, Philippe Ciuciu, Nicolas Chauffert, Pierre Weiss, Richard Kueng, Massimiliano F. Sacchi, Giacomo Mauro D’Ariano and Joel A. Tropp. Their work appears in journals such as Physical Review A, SIAM Journal on Imaging Sciences, Communications in Mathematical Physics, Random Structures and Algorithms and Foundations of Computational Mathematics.

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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