Laura Azzimonti

669 total citations
20 papers, 447 citations indexed

About

Laura Azzimonti is a scholar working on Statistics and Probability, Artificial Intelligence and Immunology. According to data from OpenAlex, Laura Azzimonti has authored 20 papers receiving a total of 447 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Statistics and Probability, 6 papers in Artificial Intelligence and 3 papers in Immunology. Recurrent topics in Laura Azzimonti's work include Statistical Methods and Inference (5 papers), Statistical Methods and Bayesian Inference (3 papers) and Bayesian Modeling and Causal Inference (3 papers). Laura Azzimonti is often cited by papers focused on Statistical Methods and Inference (5 papers), Statistical Methods and Bayesian Inference (3 papers) and Bayesian Modeling and Causal Inference (3 papers). Laura Azzimonti collaborates with scholars based in Switzerland, Italy and United States. Laura Azzimonti's co-authors include Laura M. Sangalli, Fabio Nobile, Claudia de Lalla, Maurizio Domanin, Piercesare Secchi, Paolo Dellabona, Giorgio Corani, Giulia Casorati, Francesca Mangili and Anna Maria Paganoni and has published in prestigious journals such as Journal of the American Statistical Association, Blood and The Journal of Immunology.

In The Last Decade

Laura Azzimonti

19 papers receiving 437 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laura Azzimonti Switzerland 12 147 95 51 45 40 20 447
Yanming Li United States 11 45 0.3× 15 0.2× 79 1.5× 48 1.1× 5 0.1× 45 408
J. Kenneth Tay United States 6 19 0.1× 59 0.6× 26 0.5× 48 1.1× 12 0.3× 6 456
Dai Feng United States 13 13 0.1× 57 0.6× 73 1.4× 55 1.2× 5 0.1× 41 442
Christine Porzelius Germany 14 18 0.1× 11 0.1× 56 1.1× 37 0.8× 12 0.3× 19 514
Daniel S. Herman United States 14 31 0.2× 67 0.7× 3 0.1× 50 1.1× 11 0.3× 33 682
David Tritchler Canada 12 42 0.3× 71 0.7× 56 1.1× 42 0.9× 27 649
Steve White United Kingdom 10 368 2.5× 48 0.5× 14 0.3× 12 0.3× 27 708
Haithem Taha Mohammad Ali Iraq 12 17 0.1× 28 0.3× 86 1.7× 139 3.1× 8 0.2× 23 448
Choongrak Kim South Korea 13 13 0.1× 213 2.2× 184 3.6× 26 0.6× 8 0.2× 48 782
K.J. Mason United Kingdom 15 490 3.3× 53 0.6× 8 0.2× 27 0.6× 3 0.1× 39 781

Countries citing papers authored by Laura Azzimonti

Since Specialization
Citations

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

Fields of papers citing papers by Laura Azzimonti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laura Azzimonti

This figure shows the co-authorship network connecting the top 25 collaborators of Laura Azzimonti. A scholar is included among the top collaborators of Laura Azzimonti 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 Laura Azzimonti. Laura Azzimonti 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.
Legouis, David, Anna Maria Rinaldi, Thomas Verissimo, et al.. (2024). A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease. iScience. 27(3). 109271–109271. 4 indexed citations
2.
3.
Ravasi, Damiana, Francesca Mangili, David Huber, et al.. (2022). Risk-Based Mapping Tools for Surveillance and Control of the Invasive Mosquito Aedes albopictus in Switzerland. International Journal of Environmental Research and Public Health. 19(6). 3220–3220. 6 indexed citations
4.
Delfanti, Gloria, Filippo Cortesi, Laura Azzimonti, et al.. (2022). TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells. Science Immunology. 7(74). eabn6563–eabn6563. 36 indexed citations
5.
Berjano, Pedro, Francesco Langella, David Huber, et al.. (2021). The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach. Journal of Personalized Medicine. 11(12). 1377–1377. 13 indexed citations
6.
Azzimonti, Laura, Giorgio Corani, & Marco Scutari. (2021). A Bayesian hierarchical score for structure learning from related data sets. International Journal of Approximate Reasoning. 142. 248–265. 2 indexed citations
7.
Bini, Fabiano, et al.. (2021). Artificial Intelligence in Thyroid Field—A Comprehensive Review. Cancers. 13(19). 4740–4740. 47 indexed citations
8.
Sechidis, Konstantinos, et al.. (2020). Correction to: Efficient feature selection using shrinkage estimators. Machine Learning. 109(8). 1565–1567. 2 indexed citations
9.
Sechidis, Konstantinos, et al.. (2019). Efficient feature selection using shrinkage estimators. Machine Learning. 108(8-9). 1261–1286. 22 indexed citations
10.
Azzimonti, Laura, Giorgio Corani, & Marco Zaffalon. (2019). Hierarchical estimation of parameters in Bayesian networks. Computational Statistics & Data Analysis. 137. 67–91. 16 indexed citations
11.
Azzimonti, Laura, et al.. (2018). Modeling spatially dependent functional data via regression with differential regularization. Journal of Multivariate Analysis. 170. 275–295. 22 indexed citations
12.
Gorini, Francesca, Laura Azzimonti, Gloria Delfanti, et al.. (2017). Invariant NKT cells contribute to chronic lymphocytic leukemia surveillance and prognosis. Blood. 129(26). 3440–3451. 63 indexed citations
13.
Azzimonti, Laura, Giorgio Corani, & Marco Zaffalon. (2017). Hierarchical Multinomial-Dirichlet Model for the Estimation of Conditional Probability Tables. 35. 739–744. 3 indexed citations
14.
Falla, Deborah, Francesca Mangili, Laura Azzimonti, et al.. (2017). Profiling the Location and Extent of Musicians’ Pain Using Digital Pain Drawings. Pain Practice. 18(1). 53–66. 49 indexed citations
15.
Vergara, Christian, et al.. (2015). Computational study of the fluid-dynamics in carotids before and after endarterectomy. Journal of Biomechanics. 49(1). 26–38. 33 indexed citations
16.
Azzimonti, Laura, Laura M. Sangalli, Piercesare Secchi, Maurizio Domanin, & Fabio Nobile. (2014). Blood Flow Velocity Field Estimation Via Spatial Regression With PDE Penalization. Journal of the American Statistical Association. 110(511). 1057–1071. 32 indexed citations
17.
Azzimonti, Laura, Fabio Nobile, Laura M. Sangalli, & Piercesare Secchi. (2014). Mixed Finite Elements for Spatial Regression with PDE Penalization. SIAM/ASA Journal on Uncertainty Quantification. 2(1). 305–335. 19 indexed citations
18.
Azzimonti, Laura, Francesca Ieva, & Anna Maria Paganoni. (2012). Nonlinear nonparametric mixed-effects models for unsupervised classification. Computational Statistics. 28(4). 1549–1570. 8 indexed citations
19.
Azzimonti, Laura, et al.. (2012). PDE penalization for spatial fields smoothing. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1 indexed citations
20.
Lalla, Claudia de, Anna Maria Rinaldi, Daniela Montagna, et al.. (2011). Invariant NKT Cell Reconstitution in Pediatric Leukemia Patients Given HLA-Haploidentical Stem Cell Transplantation Defines Distinct CD4+ and CD4− Subset Dynamics and Correlates with Remission State. The Journal of Immunology. 186(7). 4490–4499. 69 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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