Roland Mathis

668 citations
7 papers · 389 · 1 hit paper · h-index 5

Impact in

Papers in

Roland Mathis

6 papers receiving 383 citations

Roland Mathis's Hit Papers

Mixed-precision in-memory computing 2018 · 333 citations
3330+2+5Years since publication100200300

Peers

Roland Mathis
Comparison fields: 5 of 59
  • Electrical and Electronic Engineering 312
  • Cellular and Molecular Neuroscience 82
  • Artificial Intelligence 95
  • Hardware and Architecture 14
  • Polymers and Plastics 27
Replace Yasmin Halawani with:
Yasmin Halawani United Arab Emirates
Hyungwoo Lee South Korea
Martino Dazzi Switzerland
Boyoung Seo South Korea
Yong-Min Ju South Korea
Piergiulio Mannocci Italy
Wonbo Shim United States
Shin-Hee Han South Korea
Xingqi Zou China
Miguel Ángel Lastras-Montaño United States
Roland Mathis relative to Yasmin Halawani United Arab Emirates Yasmin Halawani's profile →
Citations per field
00.5×2.8×
Yasmin Halawani · 1×
Citations per year

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 20 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 →
2018333
2 201923
3 201715
4 202110
5 20205
6 20193
7 20240

About Roland Mathis

Roland Mathis is a scholar working on Molecular Biology, Computational Theory and Mathematics, Sociology and Political Science, Artificial Intelligence and Genetics, having authored 7 papers that have together received 389 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (3 papers), Bioinformatics and Genomic Networks (2 papers), Computational Drug Discovery Methods (2 papers), Ferroelectric and Negative Capacitance Devices (1 paper), Protein Degradation and Inhibitors (1 paper), Neural Networks and Reservoir Computing (1 paper), Advanced Proteomics Techniques and Applications (1 paper) and Mass Spectrometry Techniques and Applications (1 paper). The work is most often cited by research in Electrical and Electronic Engineering (312 citations), Cellular and Molecular Neuroscience (82 citations), Artificial Intelligence (95 citations), Hardware and Architecture (14 citations) and Polymers and Plastics (27 citations). Roland Mathis has collaborated with scholars based in Switzerland, Australia and Germany. Frequent co-authors include Matteo Manica, Alessandro Curioni, Costas Bekas, Abu Sebastian, Manuel Le Gallo, Heiner Giefers, Tomáš Tůma, Evangelos Eleftheriou, Martin Ackermann and María Rodríguez Martínez. Their work appears in journals such as Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Nature Electronics, BMC Evolutionary Biology and npj Systems Biology and Applications.

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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