Michele Guindani
- Statistics and Probability top 1%
- Statistical Methods and Inference 16
- Statistical Methods and Bayesian Inference 12
- Computational Mathematics top 10%
- Cognitive Neuroscience top 5%
- Functional Brain Connectivity Studies 11
- Neural dynamics and brain function 8
- Artificial Intelligence top 5%
- Bayesian Methods and Mixture Models 25
- Gender Studies top 5%
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- Gene expression and cancer classification 13
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- Advanced MRI Techniques and Applications 7
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- Advanced Radiotherapy Techniques 6
Michele Guindani
92 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 185
- Statistics and Probability 298
- Computational Mathematics 11
- Cognitive Neuroscience 314
- Artificial Intelligence 377
- Gender Studies 93
Countries citing papers authored by Michele Guindani
This map shows the geographic impact of Michele Guindani'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 Michele Guindani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michele Guindani more than expected).
Fields of papers citing papers by Michele Guindani
This network shows the impact of papers produced by Michele Guindani. 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 Michele Guindani. The network helps show where Michele Guindani may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Michele Guindani, 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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 0 | |
| 5 | Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience researchbreakdown → | 2021 | 272 |
| 6 | 2020 | 29 | |
| 7 | 2020 | 9 | |
| 8 | 2020 | 6 | |
| 9 | 2018 | 33 | |
| 10 | 2018 | 4 | |
| 11 | 2017 | 5 | |
| 12 | 2017 | 6 | |
| 13 | 2015 | 13 | |
| 14 | 2014 | 20 | |
| 15 | 2014 | 3 | |
| 16 | 2014 | 37 | |
| 17 | 2013 | 5 | |
| 18 | 2013 | 30 | |
| 19 | 2013 | 6 | |
| 20 | 2013 | 1 |
About Michele Guindani
Michele Guindani is a scholar working on Statistics and Probability, Artificial Intelligence and Cognitive Neuroscience, having authored 104 papers that have together received 2.0k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (25 papers), Statistical Methods and Inference (16 papers), Gene expression and cancer classification (13 papers), Statistical Methods and Bayesian Inference (12 papers), Functional Brain Connectivity Studies (11 papers), Neural dynamics and brain function (8 papers), Advanced MRI Techniques and Applications (7 papers) and Advanced Radiotherapy Techniques (6 papers). The work is most often cited by research in Statistics and Probability (298 citations), Computational Mathematics (11 citations) and Cognitive Neuroscience (314 citations). Michele Guindani has collaborated with scholars based in United States, Italy and United Kingdom. Frequent co-authors include Marina Vannucci, Alan E. Gelfand, Zhaoxia Yu, Lujia Chen, Todd C. Holmes, Xiangmin Xu, Steven F. Grieco, Jin‐Ao Duan, Brian J. Reich and Linlin Zhang. Their work appears in journals such as Biometrics, Medical Physics, Journal of the American Statistical Association, The Annals of Applied Statistics and Skeletal Radiology.
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.