Danilo Macciò

403 total citations
35 papers, 278 citations indexed

About

Danilo Macciò is a scholar working on Artificial Intelligence, Control and Systems Engineering and Numerical Analysis. According to data from OpenAlex, Danilo Macciò has authored 35 papers receiving a total of 278 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 13 papers in Control and Systems Engineering and 7 papers in Numerical Analysis. Recurrent topics in Danilo Macciò's work include Fault Detection and Control Systems (6 papers), Advanced Control Systems Optimization (6 papers) and Mathematical Approximation and Integration (6 papers). Danilo Macciò is often cited by papers focused on Fault Detection and Control Systems (6 papers), Advanced Control Systems Optimization (6 papers) and Mathematical Approximation and Integration (6 papers). Danilo Macciò collaborates with scholars based in Italy, United States and Cyprus. Danilo Macciò's co-authors include Cristiano Cervellera, Mauro Gaggero, Giacomo Boracchi, Marco Muselli, Marcello Sanguineti, A. Alessandri, Diego Carrera, Antonella Ragusa, Antonio Cataliotti and Giovanni Tinè and has published in prestigious journals such as European Journal of Operational Research, Expert Systems with Applications and Operations Research.

In The Last Decade

Danilo Macciò

34 papers receiving 268 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Danilo Macciò Italy 11 119 84 59 32 29 35 278
Joey Huchette United States 7 73 0.6× 76 0.9× 102 1.7× 31 1.0× 29 1.0× 16 388
Yinlam Chow United States 10 115 1.0× 95 1.1× 85 1.4× 30 0.9× 4 0.1× 29 314
Soroosh Shafieezadeh-Abadeh Switzerland 8 66 0.6× 62 0.7× 15 0.3× 31 1.0× 17 0.6× 15 254
Ling-po Li China 6 254 2.1× 63 0.8× 144 2.4× 157 4.9× 11 0.4× 6 485
Bart P. G. Van Parys United States 7 31 0.3× 112 1.3× 41 0.7× 19 0.6× 10 0.3× 16 260
Robin C. Gilbert United States 5 97 0.8× 52 0.6× 65 1.1× 19 0.6× 12 0.4× 11 246
Yee Leung Hong Kong 8 137 1.2× 49 0.6× 54 0.9× 41 1.3× 13 0.4× 13 326
Zhibin Deng China 10 86 0.7× 54 0.6× 54 0.9× 83 2.6× 91 3.1× 40 308
Lev Kazakovtsev Russia 9 122 1.0× 53 0.6× 37 0.6× 51 1.6× 20 0.7× 82 309
Wenjie Wang China 10 59 0.5× 52 0.6× 22 0.4× 137 4.3× 28 1.0× 42 318

Countries citing papers authored by Danilo Macciò

Since Specialization
Citations

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

Fields of papers citing papers by Danilo Macciò

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Danilo Macciò

This figure shows the co-authorship network connecting the top 25 collaborators of Danilo Macciò. A scholar is included among the top collaborators of Danilo Macciò 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 Danilo Macciò. Danilo Macciò 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.
Cervellera, Cristiano, et al.. (2023). Model Predictive Control of Port–City Traffic Interactions Over Shared Urban Infrastructure. IEEE Transactions on Control Systems Technology. 32(2). 688–695.
2.
Cervellera, Cristiano, et al.. (2022). Copula-based scenario generation for urban traffic models. Expert Systems with Applications. 210. 118389–118389. 6 indexed citations
3.
Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2021). Policy Optimization for Berth Allocation Problems. 1–6. 3 indexed citations
4.
Cervellera, Cristiano, Danilo Macciò, & Thomas Parisini. (2020). Learning Robustly Stabilizing Explicit Model Predictive Controllers: A Non-Regular Sampling Approach. IEEE Control Systems Letters. 4(3). 737–742. 7 indexed citations
5.
Cataliotti, Antonio, Cristiano Cervellera, Valentina Cosentino, et al.. (2018). An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations. IEEE Transactions on Instrumentation and Measurement. 68(2). 430–438. 23 indexed citations
6.
Boracchi, Giacomo, Diego Carrera, Cristiano Cervellera, & Danilo Macciò. (2018). QuantTree: Histograms for change detection in multivariate data streams. International Conference on Machine Learning. 2. 639–648. 17 indexed citations
7.
Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2017). Lattice point sets for state sampling in approximate dynamic programming. Optimal Control Applications and Methods. 38(6). 1193–1207. 5 indexed citations
8.
Macciò, Danilo. (2016). Local linear regression for efficient data-driven control. Knowledge-Based Systems. 98. 55–67. 2 indexed citations
9.
Cervellera, Cristiano, et al.. (2015). Efficient use of Nadaraya-Watson models and low-discrepancy sequences for approximate dynamic programming. 17. 1–8. 1 indexed citations
10.
Cervellera, Cristiano & Danilo Macciò. (2015). <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math> </inline-formula>-Discrepancy for Efficient Sampling in Approximate Dynamic Programming. IEEE Transactions on Cybernetics. 46(7). 1628–1639. 8 indexed citations
11.
Cervellera, Cristiano, et al.. (2015). Lattice point sets for efficient kernel smoothing models. 1–8. 1 indexed citations
12.
Cervellera, Cristiano, et al.. (2014). Lattice sampling for efficient learning with Nadaraya-Watson local models. 124. 1915–1922. 3 indexed citations
13.
Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2014). An analysis based on F-discrepancy for sampling in regression tree learning. 1115–1121. 4 indexed citations
14.
Cervellera, Cristiano, et al.. (2013). Quasi-random sampling for approximate dynamic programming. 1–8. 14 indexed citations
15.
Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2013). Low-discrepancy sampling for approximate dynamic programming with local approximators. Computers & Operations Research. 43. 108–115. 17 indexed citations
16.
Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2012). Efficient kernel models for learning and approximate minimization problems. Neurocomputing. 97. 74–85. 13 indexed citations
17.
Cervellera, Cristiano & Danilo Macciò. (2011). A numerical method for minimum distance estimation problems. Journal of Multivariate Analysis. 102(4). 789–800. 3 indexed citations
18.
Cervellera, Cristiano, Danilo Macciò, & Marco Muselli. (2010). Efficient global maximum likelihood estimation through kernel methods. Neural Networks. 23(7). 917–925. 4 indexed citations
19.
Cervellera, Cristiano & Danilo Macciò. (2010). A comparison of global and semi-local approximation in T-stage stochastic optimization. European Journal of Operational Research. 208(2). 109–118. 19 indexed citations
20.
Cervellera, Cristiano, Danilo Macciò, & Marco Muselli. (2008). Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks. IEEE Transactions on Neural Networks. 19(8). 1456–1467. 8 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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