Umberto Picchini

965 total citations
24 papers, 439 citations indexed

About

Umberto Picchini is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Umberto Picchini has authored 24 papers receiving a total of 439 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Statistics and Probability, 8 papers in Artificial Intelligence and 4 papers in Molecular Biology. Recurrent topics in Umberto Picchini's work include Markov Chains and Monte Carlo Methods (9 papers), Gaussian Processes and Bayesian Inference (6 papers) and Statistical Methods and Bayesian Inference (5 papers). Umberto Picchini is often cited by papers focused on Markov Chains and Monte Carlo Methods (9 papers), Gaussian Processes and Bayesian Inference (6 papers) and Statistical Methods and Bayesian Inference (5 papers). Umberto Picchini collaborates with scholars based in Denmark, Sweden and Italy. Umberto Picchini's co-authors include Susanne Ditlevsen, Andrea De Gaetano, Monica Rocco, P Pietropaoli, Andrea Morelli, Giorgio Conti, Alessandra Orecchioni, Julie Lyng Forman, Pasquale Palumbo and Benoît Beck and has published in prestigious journals such as Critical Care Medicine, Anesthesiology and Statistics in Medicine.

In The Last Decade

Umberto Picchini

23 papers receiving 416 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Umberto Picchini Denmark 12 121 69 68 65 61 24 439
Yuval Nardi Israel 9 146 1.2× 47 0.7× 19 0.3× 42 0.6× 147 2.4× 15 479
Chunfu Qiu United States 9 108 0.9× 20 0.3× 17 0.3× 26 0.4× 43 0.7× 18 458
Yousri Slaoui France 12 232 1.9× 54 0.8× 16 0.2× 29 0.4× 186 3.0× 61 458
Huiming Zhang China 14 163 1.3× 7 0.1× 29 0.4× 40 0.6× 99 1.6× 45 656
Xingyu Wang China 9 56 0.5× 55 0.8× 66 1.0× 11 0.2× 14 0.2× 16 839
Jianchang Hu United States 7 24 0.2× 5 0.1× 75 1.1× 27 0.4× 42 0.7× 13 452
Haithem Taha Mohammad Ali Iraq 12 86 0.7× 6 0.1× 87 1.3× 5 0.1× 139 2.3× 23 448
Byungjin Choi South Korea 10 91 0.8× 10 0.1× 13 0.2× 15 0.2× 40 0.7× 41 357
Dean A. Bodenham Switzerland 8 26 0.2× 6 0.1× 30 0.4× 5 0.1× 174 2.9× 10 379
Brenda MacGibbon Canada 12 31 0.3× 8 0.1× 36 0.5× 7 0.1× 12 0.2× 32 539

Countries citing papers authored by Umberto Picchini

Since Specialization
Citations

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

Fields of papers citing papers by Umberto Picchini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Umberto Picchini

This figure shows the co-authorship network connecting the top 25 collaborators of Umberto Picchini. A scholar is included among the top collaborators of Umberto Picchini 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 Umberto Picchini. Umberto Picchini 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.
Picchini, Umberto & Massimiliano Tamborrino. (2024). Guided Sequential ABC Schemes for Intractable Bayesian Models. Bayesian Analysis. 20(4). 3 indexed citations
2.
Golightly, Andrew, et al.. (2024). Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations. Bayesian Analysis. 21(1). 1 indexed citations
3.
Picchini, Umberto, et al.. (2023). Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality. Statistics in Medicine. 42(23). 4128–4146.
4.
Shashkova, Sviatlana, et al.. (2022). Scalable and flexible inference framework for stochastic dynamic single-cell models. PLoS Computational Biology. 18(5). e1010082–e1010082. 8 indexed citations
5.
Picchini, Umberto, et al.. (2022). Sequentially Guided MCMC Proposals for Synthetic Likelihoods and Correlated Synthetic Likelihoods. Bayesian Analysis. 18(4). 2 indexed citations
6.
Corander, Jukka, et al.. (2020). Adaptive MCMC for synthetic likelihoods and correlated synthetic likelihoods. Bayesian Analysis. 1 indexed citations
7.
Picchini, Umberto & Julie Lyng Forman. (2019). Bayesian Inference for Stochastic Differential Equation Mixed Effects Models of a Tumour Xenography Study. Journal of the Royal Statistical Society Series C (Applied Statistics). 68(4). 887–913. 12 indexed citations
8.
Picchini, Umberto. (2018). Likelihood-free stochastic approximation EM for inference in complex models. Communications in Statistics - Simulation and Computation. 48(3). 861–881. 1 indexed citations
9.
Picchini, Umberto & Julie Lyng Forman. (2016). Stochastic differential equation mixed effects models for tumor growth and response to treatment. arXiv (Cornell University). 3 indexed citations
10.
Picchini, Umberto, et al.. (2016). Approximate maximum likelihood estimation using data-cloning ABC. Computational Statistics & Data Analysis. 105. 166–183. 7 indexed citations
11.
Picchini, Umberto. (2013). Inference for SDE Models via Approximate Bayesian Computation. Journal of Computational and Graphical Statistics. 23(4). 1080–1100. 33 indexed citations
12.
Picchini, Umberto & Susanne Ditlevsen. (2010). Practical estimation of high dimensional stochastic differential mixed-effects models. Computational Statistics & Data Analysis. 55(3). 1426–1444. 40 indexed citations
13.
Picchini, Umberto, Andrea De Gaetano, & Susanne Ditlevsen. (2009). Stochastic Differential Mixed-Effects Models. Scandinavian Journal of Statistics. 37(1). 67–90. 45 indexed citations
14.
Palumbo, Pasquale, et al.. (2008). A general approach to the apparent permeability index. Journal of Pharmacokinetics and Pharmacodynamics. 35(2). 235–248. 38 indexed citations
15.
Picchini, Umberto, Susanne Ditlevsen, & Andrea De Gaetano. (2008). Maximum likelihood estimation of a time-inhomogeneous stochastic differential model of glucose dynamics. Mathematical Medicine and Biology A Journal of the IMA. 25(2). 141–155. 19 indexed citations
16.
Picchini, Umberto. (2007). SDE Toolbox: Simulation and estimation of stochastic differential equations with MATLAB.. Lund University Publications (Lund University). 36 indexed citations
17.
Picchini, Umberto, Susanne Ditlevsen, & Andrea De Gaetano. (2006). Modeling the euglycemic hyperinsulinemic clamp by stochastic differential equations. Journal of Mathematical Biology. 53(5). 771–796. 29 indexed citations
18.
Morelli, Andrea, Zaccaria Ricci, Rinaldo Bellomo, et al.. (2005). Prophylactic fenoldopam for renal protection in sepsis: A randomized, double-blind, placebo-controlled pilot trial*. Critical Care Medicine. 33(11). 2451–2456. 71 indexed citations
19.
Picchini, Umberto, Andrea De Gaetano, Simona Panunzi, Susanne Ditlevsen, & Geltrude Mingrone. (2005). A mathematical model of the euglycemic hyperinsulinemic clamp. Theoretical Biology and Medical Modelling. 2(1). 44–44. 23 indexed citations
20.
Gaetano, Andrea De, Giuliana Cortese, Morten Gram Pedersen, et al.. (2004). Modeling Serum Creatinine in Septic ICU Patients. IRIS Research product catalog (Sapienza University of Rome). 4(2). 173–180. 1 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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