Fabio Stella

1.8k total citations
72 papers, 996 citations indexed

About

Fabio Stella is a scholar working on Artificial Intelligence, Management Science and Operations Research and Information Systems. According to data from OpenAlex, Fabio Stella has authored 72 papers receiving a total of 996 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 19 papers in Management Science and Operations Research and 11 papers in Information Systems. Recurrent topics in Fabio Stella's work include Bayesian Modeling and Causal Inference (15 papers), Topic Modeling (8 papers) and Advanced Bandit Algorithms Research (8 papers). Fabio Stella is often cited by papers focused on Bayesian Modeling and Causal Inference (15 papers), Topic Modeling (8 papers) and Advanced Bandit Algorithms Research (8 papers). Fabio Stella collaborates with scholars based in Italy, Austria and Switzerland. Fabio Stella's co-authors include Markus Zanker, Mauro Scanagatta, Antonio Salmerón, Alexei A. Gaivoronski, Francesca Arcelli Fontana, D. Magatti, Marco Zanoni, Elif Özkırımlı, Y. A. Amer and Ramón Brena and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and European Journal of Operational Research.

In The Last Decade

Fabio Stella

67 papers receiving 945 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fabio Stella Italy 18 469 220 220 87 74 72 996
Xun Liang China 15 507 1.1× 197 0.9× 190 0.9× 47 0.5× 69 0.9× 123 1.0k
Giuliano Armano Italy 15 415 0.9× 120 0.5× 227 1.0× 146 1.7× 55 0.7× 94 942
Enza Messina Italy 18 480 1.0× 109 0.5× 103 0.5× 126 1.4× 52 0.7× 87 1.1k
Luca Cagliero Italy 18 581 1.2× 370 1.7× 140 0.6× 51 0.6× 60 0.8× 127 1.1k
Yongjun Li China 22 339 0.7× 259 1.2× 619 2.8× 52 0.6× 179 2.4× 91 1.5k
İsmail Hakkı Toroslu Türkiye 18 315 0.7× 307 1.4× 208 0.9× 27 0.3× 156 2.1× 86 1.1k
Herna L. Viktor Canada 14 656 1.4× 203 0.9× 121 0.6× 186 2.1× 84 1.1× 85 1.3k
Dawei Cheng China 17 597 1.3× 181 0.8× 255 1.2× 18 0.2× 74 1.0× 75 1.1k
Michael Kirley Australia 19 895 1.9× 251 1.1× 359 1.6× 47 0.5× 288 3.9× 103 1.9k
Spiridon Likothanassis Greece 15 246 0.5× 106 0.5× 163 0.7× 89 1.0× 87 1.2× 76 653

Countries citing papers authored by Fabio Stella

Since Specialization
Citations

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

Fields of papers citing papers by Fabio Stella

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabio Stella

This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Stella. A scholar is included among the top collaborators of Fabio Stella based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fabio Stella. Fabio Stella is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Bernasconi, Alice, Peter Lucas, Marco Scutari, et al.. (2024). From Real-World Data to Causally Interpretable Models: A Bayesian Network to Predict Cardiovascular Diseases in Adolescents and Young Adults with Breast Cancer. Cancers. 16(21). 3643–3643. 1 indexed citations
3.
Stella, Fabio, et al.. (2024). Multi-armed bandits for performance marketing. International Journal of Data Science and Analytics. 20(1). 151–165.
4.
Marelli, Marco, et al.. (2024). Surfing the OCEAN: The machine learning psycholexical approach 2.0 to detect personality traits in texts. Journal of Personality. 92(6). 1602–1615. 2 indexed citations
5.
Baric, Sanja, et al.. (2023). Development of a Knowledge-Based Expert System for Diagnosing Post-Harvest Diseases of Apple. Agriculture. 13(1). 177–177. 4 indexed citations
6.
Galazzo, Gianluca, Niels van Best, Monique Mommers, et al.. (2023). Edge and modular significance assessment in individual-specific networks. Scientific Reports. 13(1). 7868–7868. 2 indexed citations
7.
Scutari, Marco, et al.. (2021). A constraint-based algorithm for the structural learning of continuous-time Bayesian networks. BOA (University of Milano-Bicocca). 5 indexed citations
8.
Acerbi, Enzo, Marcela Hortová Kohoutková, Tsokyi Choera, et al.. (2020). Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism. Journal of Fungi. 6(3). 108–108. 2 indexed citations
9.
Scanagatta, Mauro, Antonio Salmerón, & Fabio Stella. (2019). A survey on Bayesian network structure learning from data. Progress in Artificial Intelligence. 8(4). 425–439. 156 indexed citations
10.
Stella, Fabio, et al.. (2019). A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity. Artificial Intelligence in Medicine. 95. 104–117. 21 indexed citations
11.
Acerbi, Enzo, Elena Viganò, Michael Poidinger, et al.. (2016). Continuous time Bayesian networks identify Prdm1 as a negative regulator of TH17 cell differentiation in humans. Scientific Reports. 6(1). 23128–23128. 10 indexed citations
12.
Stella, Fabio, et al.. (2014). Learning continuous time Bayesian network classifiers. International Journal of Approximate Reasoning. 55(8). 1728–1746. 7 indexed citations
13.
Acerbi, Enzo, Teresa Zelante, Vipin Narang, & Fabio Stella. (2014). Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation. BMC Bioinformatics. 15(1). 387–387. 19 indexed citations
14.
Stella, Fabio, et al.. (2013). Conditional Log-Likelihood for Continuous Time Bayesian Network Classifiers. BOA (University of Milano-Bicocca). 8399. 35–50. 3 indexed citations
15.
Stella, Fabio & Y. A. Amer. (2012). Continuous time Bayesian network classifiers. Journal of Biomedical Informatics. 45(6). 1108–1119. 25 indexed citations
16.
Fraccalvieri, Doménico, Alessandro Pandini, Fabio Stella, & Laura Bonati. (2011). Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps. BMC Bioinformatics. 12(1). 158–158. 39 indexed citations
17.
Tuana, Giacomo, Viola Volpato, Paola Ricciardi‐Castagnoli, et al.. (2011). Classification of dendritic cell phenotypes from gene expression data. BMC Immunology. 12(1). 50–50. 7 indexed citations
18.
Gaivoronski, Alexei A. & Fabio Stella. (2003). On-line portfolio selection using stochastic programming. Journal of Economic Dynamics and Control. 27(6). 1013–1043. 27 indexed citations
19.
Gaivoronski, Alexei A. & Fabio Stella. (2000). Nonstationary Optimization Approach for Finding Universal Portfolios. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 1 indexed citations
20.
McKeown, John, et al.. (1997). Some numerical aspects of the training problem for feed-forward neural nets. Neural Networks. 10(8). 1455–1463. 18 indexed citations

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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