Roland Mathis

689 citations
7 papers · 407 · 1 hit paper · h-index 5

Impact in

Papers in

    • Bioinformatics and Genomic Networks 2
    • Genomics and Chromatin Dynamics 1
    • Gene expression and cancer classification 1
    • Gene Regulatory Network Analysis 1
    • Neural Networks and Reservoir Computing 1

Roland Mathis

7 papers receiving 402 citations

Roland Mathis's Hit Papers

Mixed-precision in-memory computing 2018 · 347 citations
3470+2+5Years since publication100200300

Peers

Roland Mathis
Comparison fields: 5 of 57
  • Electrical and Electronic Engineering 318
  • Cellular and Molecular Neuroscience 83
  • Artificial Intelligence 96
  • Hardware and Architecture 14
  • Polymers and Plastics 27
Replace Hyungwoo Lee with:
Hyungwoo Lee South Korea
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Wonbo Shim United States
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Piergiulio Mannocci Italy
Yasmin Halawani United Arab Emirates
Shin-Hee Han South Korea
Evgeny Pikhay Israel
Jun Deguchi Japan
Roland Mathis relative to Hyungwoo Lee South Korea Hyungwoo Lee's profile →
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Countries citing papers authored by Roland Mathis

Since Specialization
Citations

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

Fields of papers citing papers by Roland Mathis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Roland Mathis, 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 Roland Mathis Line = papers co-authored together Roland Mathis links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown
#Work
1
Mixed-precision in-memory computing
Hit paper breakdown →
2018347
2 201923
3 201715
4 202113
5 20205
6 20193
7 20241

About Roland Mathis

Roland Mathis is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Spectroscopy and Electrical and Electronic Engineering, having authored 7 papers that have together received 407 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (2 papers), Genomics and Chromatin Dynamics (1 paper), Advanced Proteomics Techniques and Applications (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper), Computational Drug Discovery Methods (1 paper), Gene expression and cancer classification (1 paper), Gene Regulatory Network Analysis (1 paper) and Neural Networks and Reservoir Computing (1 paper). The work is most often cited by research in Electrical and Electronic Engineering (318 citations), Cellular and Molecular Neuroscience (83 citations), Artificial Intelligence (96 citations), Hardware and Architecture (14 citations) and Polymers and Plastics (27 citations). Roland Mathis has collaborated with scholars based in Switzerland, Germany and China. Frequent co-authors include Matteo Manica, Heiner Giefers, Manuel Le Gallo, Abu Sebastian, Costas Bekas, Alessandro Curioni, Evangelos Eleftheriou, Tomáš Tůma, Martin Ackermann and María Rodríguez Martínez. Their work appears in journals such as Bioinformatics, npj Systems Biology and Applications, Nature Electronics, BMC Evolutionary Biology and IEEE/ACM Transactions on Computational Biology and Bioinformatics.

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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