Nicola Prezza
Impact in
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- Network Packet Processing and Optimization
- Artificial Intelligence top 10%
- Algorithms and Data Compression
- Natural Language Processing Techniques
Papers in
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- Algorithms and Data Compression 17
- Natural Language Processing Techniques 5
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- semigroups and automata theory 11
- Cellular Automata and Applications 3
- Co-authors
- Alberto Policriti (7 shared papers)Gonzalo Navarro (3 shared papers)Giovanna Rosone (4 shared papers)Francesco Vezzi (3 shared papers)Marinella Sciortino (3 shared papers)Nadia Pisanti (2 shared papers)Tomasz Kociumaka (1 shared paper)Max Käller (2 shared papers)
In The Last Decade
Nicola Prezza
22 papers receiving 170 citations
Peers
Comparison fields: 5 of 41
- Hardware and Architecture 23
- Artificial Intelligence 101
- Computational Theory and Mathematics 35
- Environmental Chemistry 15
- Computer Graphics and Computer-Aided Design 4
Countries citing papers authored by Nicola Prezza
This map shows the geographic impact of Nicola Prezza'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 Nicola Prezza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicola Prezza more than expected).
Fields of papers citing papers by Nicola Prezza
This network shows the impact of papers produced by Nicola Prezza. 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 Nicola Prezza. The network helps show where Nicola Prezza may publish in the future.
Co-authors
The 25 scholars most cited alongside Nicola Prezza, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 26 | |
| 2 | 2017 | 17 | |
| 3 | 2019 | 17 | |
| 4 | 2012 | 16 | |
| 5 | 2022 | 13 | |
| 6 | 2018 | 13 | |
| 7 | 2020 | 9 | |
| 8 | 2020 | 7 | |
| 9 | 2020 | 7 | |
| 10 | 2021 | 7 | |
| 11 | 2016 | 6 | |
| 12 | 2017 | 6 | |
| 13 | 2018 | 5 | |
| 14 | 2017 | 5 | |
| 15 | 2015 | 5 | |
| 16 | 2019 | 3 | |
| 17 | 2016 | 3 | |
| 18 | 2024 | 2 | |
| 19 | 2019 | 1 | |
| 20 | 2023 | 1 |
About Nicola Prezza
Nicola Prezza is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology, Hardware and Architecture and Organic Chemistry, having authored 24 papers that have together received 171 indexed citations. Recurring topics across this work include Algorithms and Data Compression (17 papers), semigroups and automata theory (11 papers), Network Packet Processing and Optimization (5 papers), Natural Language Processing Techniques (5 papers), DNA and Biological Computing (3 papers), Cellular Automata and Applications (3 papers), Epigenetics and DNA Methylation (2 papers) and RNA modifications and cancer (1 paper). The work is most often cited by research in Hardware and Architecture (23 citations), Artificial Intelligence (101 citations), Computational Theory and Mathematics (35 citations), Environmental Chemistry (15 citations) and Computer Graphics and Computer-Aided Design (4 citations). Nicola Prezza has collaborated with scholars based in Italy, Denmark and Chile. Frequent co-authors include Alberto Policriti, Gonzalo Navarro, Giovanna Rosone, Francesco Vezzi, Marinella Sciortino, Nadia Pisanti, Tomasz Kociumaka, Max Käller, Francesco Marabita and Philip Bille. Their work appears in journals such as BMC Bioinformatics, Algorithms for Molecular Biology, Journal of the ACM, Algorithmica and Theoretical Computer Science.
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.