Antonio Candelieri

3.1k total citations
107 papers, 2.0k citations indexed

About

Antonio Candelieri is a scholar working on Civil and Structural Engineering, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Antonio Candelieri has authored 107 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Civil and Structural Engineering, 32 papers in Artificial Intelligence and 17 papers in Computational Theory and Mathematics. Recurrent topics in Antonio Candelieri's work include Water Systems and Optimization (24 papers), Advanced Multi-Objective Optimization Algorithms (17 papers) and Machine Learning and Data Classification (11 papers). Antonio Candelieri is often cited by papers focused on Water Systems and Optimization (24 papers), Advanced Multi-Objective Optimization Algorithms (17 papers) and Machine Learning and Data Classification (11 papers). Antonio Candelieri collaborates with scholars based in Italy, United Kingdom and United States. Antonio Candelieri's co-authors include Francesco Archetti, Ilaria Giordani, G Dolce, Domenico Conforti, Francesco Riganello, Francesca Bandinelli, Marco Matucci‐Cerinic, Luca Morganti, Fabrizia Mantovani and Federica Pallavicini and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Antonio Candelieri

97 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Antonio Candelieri Italy 26 416 273 260 242 211 107 2.0k
Mingchao Li China 32 1.3k 3.2× 278 1.0× 30 0.1× 213 0.9× 66 0.3× 230 3.4k
Rahul Rai United States 32 269 0.6× 300 1.1× 67 0.3× 40 0.2× 23 0.1× 156 3.8k
Juha Röning Finland 25 207 0.5× 388 1.4× 32 0.1× 59 0.2× 32 0.2× 274 2.7k
Zaffar Ahmed Shaikh Pakistan 26 23 0.1× 447 1.6× 728 2.8× 40 0.2× 141 0.7× 132 3.1k
Fan Tang China 32 50 0.1× 288 1.1× 64 0.2× 40 0.2× 83 0.4× 177 4.1k
Liz Varga United Kingdom 29 184 0.4× 58 0.2× 41 0.2× 76 0.3× 215 1.0× 143 3.0k
Lu Jia China 30 42 0.1× 158 0.6× 98 0.4× 90 0.4× 16 0.1× 139 3.6k
Alice Z. Chuang United States 28 49 0.1× 156 0.6× 92 0.4× 24 0.1× 11 0.1× 124 4.3k
Hainan Chen China 26 193 0.5× 155 0.6× 26 0.1× 31 0.1× 32 0.2× 66 2.9k
Zhifang Pan China 32 42 0.1× 1.3k 4.8× 45 0.2× 52 0.2× 40 0.2× 92 3.0k

Countries citing papers authored by Antonio Candelieri

Since Specialization
Citations

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

Fields of papers citing papers by Antonio Candelieri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Antonio Candelieri

This figure shows the co-authorship network connecting the top 25 collaborators of Antonio Candelieri. A scholar is included among the top collaborators of Antonio Candelieri 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 Antonio Candelieri. Antonio Candelieri 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.
Ponti, Andrea, et al.. (2024). Prompt Optimization in Large Language Models. Mathematics. 12(6). 929–929. 8 indexed citations
3.
Archetti, Francesco, et al.. (2024). Bayesian Optimization for Instruction Generation. Applied Sciences. 14(24). 11865–11865.
4.
Candelieri, Antonio, Andrea Ponti, & Francesco Archetti. (2024). Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization. Machine Learning. 113(5). 2701–2731. 7 indexed citations
5.
Ponti, Andrea, Ilaria Giordani, Antonio Candelieri, & Francesco Archetti. (2024). Wasserstein-Enabled Leaks Localization in Water Distribution Networks. Water. 16(3). 412–412.
6.
Pau, Danilo, et al.. (2024). Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network. Algorithms. 17(1). 22–22. 1 indexed citations
7.
Ponti, Andrea, et al.. (2024). Bayesian Optimization Using Simulation-Based Multiple Information Sources over Combinatorial Structures. SHILAP Revista de lepidopterología. 6(4). 2232–2247. 1 indexed citations
8.
Candelieri, Antonio, Andrea Ponti, & Francesco Archetti. (2023). Uncertainty quantification and exploration–exploitation trade-off in humans. BOA (University of Milano-Bicocca). 1 indexed citations
9.
Ponti, Andrea, Antonio Candelieri, Ilaria Giordani, & Francesco Archetti. (2023). Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms. Mathematics. 11(10). 2342–2342. 1 indexed citations
10.
Candelieri, Antonio, et al.. (2022). AutoTinyML for microcontrollers: Dealing with black-box deployability. Expert Systems with Applications. 207. 117876–117876. 7 indexed citations
11.
Candelieri, Antonio, Andrea Ponti, & Francesco Archetti. (2022). Explaining Exploration–Exploitation in Humans. Big Data and Cognitive Computing. 6(4). 155–155.
12.
Candelieri, Antonio, et al.. (2021). Green machine learning via augmented Gaussian processes and multi-information source optimization. BOA (University of Milano-Bicocca). 14 indexed citations
13.
Fersini, Elisabetta, et al.. (2021). OCTIS: Comparing and Optimizing Topic models is Simple!. INFM-OAR (INFN Catania). 263–270. 50 indexed citations
14.
Candelieri, Antonio, et al.. (2020). Energy Efficient Hyperparameters Tuning through Augmented Gaussian Processes and Multi-information Source optimization. BOA (University of Milano-Bicocca). 34–38. 2 indexed citations
15.
Candelieri, Antonio, et al.. (2020). Modelling human active search in optimizing black-box functions. BOA (University of Milano-Bicocca). 5 indexed citations
16.
Candelieri, Antonio. (2019). Sequential model based optimization of partially defined functions under unknown constraints. Journal of Global Optimization. 79(2). 281–303. 32 indexed citations
17.
Pedrielli, Giulia, et al.. (2018). Stochastic optimization for feasibility determination: an application to water pump operation in water distribution network. Winter Simulation Conference. 1945–1956. 3 indexed citations
18.
Candelieri, Antonio, Maria Daniela Cortese, G Dolce, Francesco Riganello, & Walter G. Sannita. (2011). Visual Pursuit: Within-Day Variability in the Severe Disorder of Consciousness. Journal of Neurotrauma. 28(10). 2013–2017. 62 indexed citations
19.
Bandinelli, Francesca, Andrea Delle Sedie, Lucrezia Riente, et al.. (2010). VARIATION DURING DIFFERENT DECADES OF DIAGNOSTIC AND THERAPEUTIC DELAY IN PATIENTS OF ANKYLOSING SPONDYLITIS (AS). Clinical and Experimental Rheumatology. 28(4). 628–628. 1 indexed citations
20.
Colantonio, Sara, Massimo Martinelli, Davide Moroni, et al.. (2008). A Decision Support Resource as a Kernel of a Semantic Web Based Platform Oriented to Heart Failure.. BOA (University of Milano-Bicocca). 142–148. 3 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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