Georg Hahn

714 citations
39 papers · 190 · h-index 8

Impact in

Papers in

Georg Hahn

31 papers receiving 185 citations

Peers

Georg Hahn
Comparison fields: 5 of 73
  • Computational Mathematics 2
  • Artificial Intelligence 87
  • Computational Theory and Mathematics 30
  • Statistics and Probability 12
  • Biomaterials 12
Replace Min Sha with:
Min Sha Australia
Asia Ivić Weiss Canada
E. Fischer Israel
Chaim Goodman-Strauss United States
T. H. Yang Taiwan
Karl Wimmer United States
Matthias Schulte Germany
Alexander Semenov Russia
Cheng-Fa Tsai Taiwan
Georg Hahn relative to Min Sha Australia Min Sha's profile →
Citations per field
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Citations per year

Countries citing papers authored by Georg Hahn

Since Specialization
Citations

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

Fields of papers citing papers by Georg Hahn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Georg Hahn, 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 Georg Hahn Line = papers co-authored together Georg Hahn links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201838
2 202320
3 200916
4 202512
5 202211
6 201711
7
MMCTest – A Safe Algorithm for Implementing Multiple Monte Carlo Tests
201410
8 20228
9 20237
10 20205
11 20225
12 20235
13 20175
14 20204
15 20244
16 20214
17 20214
18 20252
19 20242
20 20242

About Georg Hahn

Georg Hahn is a scholar working on Statistics and Probability, Molecular Biology, Artificial Intelligence, Genetics and Computational Theory and Mathematics, having authored 39 papers that have together received 190 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (7 papers), Statistical Methods and Inference (7 papers), Quantum Information and Cryptography (6 papers), Genetic Associations and Epidemiology (5 papers), Quantum-Dot Cellular Automata (4 papers), Statistical Methods in Clinical Trials (4 papers), Genetic and phenotypic traits in livestock (3 papers) and Epigenetics and DNA Methylation (3 papers). The work is most often cited by research in Computational Mathematics (2 citations), Artificial Intelligence (87 citations), Computational Theory and Mathematics (30 citations), Statistics and Probability (12 citations) and Biomaterials (12 citations). Georg Hahn has collaborated with scholars based in United States, United Kingdom and South Korea. Frequent co-authors include Hristo Djidjev, Elijah Pelofske, Guillaume Chapuis, Guillaume Rizk, Axel Gandy, Wolfgang Gindl‐Altmutter, Johannes Konnerth, Christoph Lange, Sharon M. Lutz and Idris A. Eckley. Their work appears in journals such as Genetic Epidemiology, Statistics and Computing, Briefings in Bioinformatics, BMC Bioinformatics and Alzheimer s & Dementia.

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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