Mauro Scanagatta

About

Mauro Scanagatta is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics. According to data from OpenAlex, Mauro Scanagatta has authored 9 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Management Science and Operations Research and 1 paper in Computational Theory and Mathematics. Recurrent topics in Mauro Scanagatta’s work include Bayesian Modeling and Causal Inference (8 papers), Data Quality and Management (5 papers) and Machine Learning and Data Classification (2 papers). Mauro Scanagatta is often cited by papers focused on Bayesian Modeling and Causal Inference (8 papers), Data Quality and Management (5 papers) and Machine Learning and Data Classification (2 papers). Mauro Scanagatta collaborates with scholars based in Switzerland, United Kingdom and Italy. Mauro Scanagatta's co-authors include Giorgio Corani, Antonio Salmerón, Fabio Stella, Marco Zaffalon, Cassio P. de Campos, Raman Kazhamiakin, Annapaola Marconi, U Kang and Jaemin Yoo and has published in prestigious journals such as Artificial Intelligence, IEEE Transactions on Intelligent Transportation Systems and Machine Learning.

In The Last Decade

Co-authorship network of co-authors of Mauro Scanagatta i

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Fields of papers citing papers by Mauro Scanagatta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Mauro Scanagatta

Since Specialization

Citations

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

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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