Sofia Fernandes
Impact in
- Computational Mathematics top 5%
- Tensor decomposition and applications
Papers in
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- Algorithms and Data Compression 4
- Computational Physics and Python Applications 3
- Advanced Graph Neural Networks 2
- Bayesian Modeling and Causal Inference 1
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- Tensor decomposition and applications 7
- Co-authors
- João Manuel R. S. Tavares (1 shared paper)Shazia Tabassum (1 shared paper)Fabíola S. F. Pereira (1 shared paper)João Gama (6 shared papers)Hadi Fanaee‐T (5 shared papers)Diogo Gomes (4 shared papers)Rui L. Aguiar (4 shared papers)Mário Antunes (4 shared papers)
In The Last Decade
Sofia Fernandes
11 papers receiving 281 citations
Peers
Comparison fields: 5 of 98
- Computational Mathematics 27
- Medical Laboratory Technology 6
- Statistical and Nonlinear Physics 41
- Transportation 14
- Communication 14
Countries citing papers authored by Sofia Fernandes
This map shows the geographic impact of Sofia Fernandes'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 Sofia Fernandes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sofia Fernandes more than expected).
Fields of papers citing papers by Sofia Fernandes
This network shows the impact of papers produced by Sofia Fernandes. 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 Sofia Fernandes. The network helps show where Sofia Fernandes may publish in the future.
Co-authors
The 11 scholars most cited alongside Sofia Fernandes, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 190 | |
| 2 | 2020 | 33 | |
| 3 | 2020 | 17 | |
| 4 | 2018 | 11 | |
| 5 | 2021 | 11 | |
| 6 | 2020 | 8 | |
| 7 | 2017 | 7 | |
| 8 | 2021 | 5 | |
| 9 | 2021 | 2 | |
| 10 | 2021 | 1 | |
| 11 | 2021 | 1 |
About Sofia Fernandes
Sofia Fernandes is a scholar working on Artificial Intelligence, Computational Mathematics, Signal Processing, Building and Construction and Statistical and Nonlinear Physics, having authored 11 papers that have together received 286 indexed citations. Recurring topics across this work include Tensor decomposition and applications (7 papers), Algorithms and Data Compression (4 papers), Computational Physics and Python Applications (3 papers), Advanced Graph Neural Networks (2 papers), Complex Network Analysis Techniques (1 paper), Time Series Analysis and Forecasting (1 paper), Advanced Statistical Process Monitoring (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Computational Mathematics (27 citations), Medical Laboratory Technology (6 citations), Statistical and Nonlinear Physics (41 citations), Transportation (14 citations) and Communication (14 citations). Sofia Fernandes has collaborated with scholars based in Portugal, Sweden and Croatia. Frequent co-authors include João Manuel R. S. Tavares, Shazia Tabassum, Fabíola S. F. Pereira, João Gama, Hadi Fanaee‐T, Diogo Gomes, Rui L. Aguiar, Mário Antunes, João Paulo Barraca and Tonči Carić. Their work appears in journals such as Machine Learning, Applied Sciences, Data Mining and Knowledge Discovery, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery and Engineering Applications of Artificial Intelligence.
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.