Ferdinando Cicalese

1.4k total citations
61 papers, 367 citations indexed

About

Ferdinando Cicalese is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computational Theory and Mathematics. According to data from OpenAlex, Ferdinando Cicalese has authored 61 papers receiving a total of 367 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 27 papers in Computer Networks and Communications and 20 papers in Computational Theory and Mathematics. Recurrent topics in Ferdinando Cicalese's work include Optimization and Search Problems (22 papers), Machine Learning and Algorithms (18 papers) and Algorithms and Data Compression (15 papers). Ferdinando Cicalese is often cited by papers focused on Optimization and Search Problems (22 papers), Machine Learning and Algorithms (18 papers) and Algorithms and Data Compression (15 papers). Ferdinando Cicalese collaborates with scholars based in Italy, Brazil and Germany. Ferdinando Cicalese's co-authors include Ugo Vaccaro, Eduardo Sany Laber, Martin Milanič, Luisa Gargano, Zsuzsanna Lipták, Gabriele Fici, Christian Deppe, Daniele Mundici, Gennaro Cordasco and Vincenzo Loia and has published in prestigious journals such as IEEE Transactions on Information Theory, Fuzzy Sets and Systems and Journal of the ACM.

In The Last Decade

Ferdinando Cicalese

56 papers receiving 356 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ferdinando Cicalese Italy 10 213 140 125 66 33 61 367
Narayana Santhanam United States 10 391 1.8× 166 1.2× 81 0.6× 107 1.6× 25 0.8× 47 514
Raphaël Clifford United Kingdom 10 281 1.3× 109 0.8× 59 0.5× 89 1.3× 16 0.5× 40 437
Huy L. Nguyên United States 11 241 1.1× 159 1.1× 92 0.7× 58 0.9× 16 0.5× 42 481
Amin Coja‐Oghlan Germany 16 200 0.9× 321 2.3× 211 1.7× 28 0.4× 88 2.7× 75 670
Elena Grigorescu United States 9 204 1.0× 169 1.2× 86 0.7× 39 0.6× 15 0.5× 48 312
Fred Piper United Kingdom 13 294 1.4× 136 1.0× 156 1.2× 33 0.5× 10 0.3× 58 705
Hariharan Narayanan United States 11 126 0.6× 163 1.2× 69 0.6× 23 0.3× 27 0.8× 42 443
Christian Deppe Germany 9 153 0.7× 65 0.5× 92 0.7× 62 0.9× 15 0.5× 94 349
Lorenzo Orecchia United States 11 141 0.7× 167 1.2× 109 0.9× 50 0.8× 62 1.9× 21 406

Countries citing papers authored by Ferdinando Cicalese

Since Specialization
Citations

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

Fields of papers citing papers by Ferdinando Cicalese

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ferdinando Cicalese

This figure shows the co-authorship network connecting the top 25 collaborators of Ferdinando Cicalese. A scholar is included among the top collaborators of Ferdinando Cicalese 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 Ferdinando Cicalese. Ferdinando Cicalese 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.
Souza, Victor L. F., Ferdinando Cicalese, Eduardo Sany Laber, & Marco Molinaro. (2025). Decision trees with short explainable rules. Theoretical Computer Science. 1047. 115344–115344. 1 indexed citations
2.
Marchesini, Enrico, et al.. (2024). Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees. Proceedings of the AAAI Conference on Artificial Intelligence. 38(19). 21387–21394. 2 indexed citations
3.
Cicalese, Ferdinando, et al.. (2023). Hardness and approximation of multiple sequence alignment with column score. Theoretical Computer Science. 946. 113683–113683.
4.
Cicalese, Ferdinando, et al.. (2023). The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks. 217–224. 3 indexed citations
5.
Cicalese, Ferdinando, et al.. (2019). New results on information theoretic clustering. International Conference on Machine Learning. 1242–1251. 2 indexed citations
6.
Cicalese, Ferdinando, Zsuzsanna Lipták, & Massimiliano Rossi. (2018). Bubble-Flip—A new generation algorithm for prefix normal words. Theoretical Computer Science. 743. 38–52. 1 indexed citations
7.
Cicalese, Ferdinando, Luisa Gargano, & Ugo Vaccaro. (2016). Approximating probability distributions with short vectors, via information theoretic distance measures. 1138–1142. 4 indexed citations
8.
Cicalese, Ferdinando, Martin Milanič, & Roméo Rizzi. (2015). On the complexity of the vector connectivity problem. Theoretical Computer Science. 591. 60–71.
9.
Cicalese, Ferdinando, et al.. (2014). Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost. International Conference on Machine Learning. 414–422. 7 indexed citations
10.
Cicalese, Ferdinando, Gennaro Cordasco, Luisa Gargano, Martin Milanič, & Ugo Vaccaro. (2014). Latency-bounded target set selection in social networks. Theoretical Computer Science. 535. 1–15. 33 indexed citations
11.
Cicalese, Ferdinando, et al.. (2013). Information Theory, Combinatorics, and Search Theory: in memory of Rudolf Ahlswede. Springer eBooks. 5 indexed citations
12.
Cicalese, Ferdinando, Eduardo Sany Laber, Oren Weimann, & Raphael Yuster. (2013). Approximating the maximum consecutive subsums of a sequence. Theoretical Computer Science. 525. 130–137. 2 indexed citations
13.
Cicalese, Ferdinando, Martin Milanič, & Ugo Vaccaro. (2012). On the approximability and exact algorithms for vector domination and related problems in graphs. Discrete Applied Mathematics. 161(6). 750–767. 15 indexed citations
14.
Cicalese, Ferdinando, et al.. (2012). The binary identification problem for weighted trees. Theoretical Computer Science. 459. 100–112. 7 indexed citations
15.
Cicalese, Ferdinando, et al.. (2011). On the complexity of searching in trees and partially ordered structures. Theoretical Computer Science. 412(50). 6879–6896. 10 indexed citations
16.
Cicalese, Ferdinando, Gabriele Fici, & Zsuzsanna Lipták. (2009). Searching for Jumbled Patterns in Strings. Nova Science Publishers (Nova Science Publishers, Inc.). 105–117. 16 indexed citations
17.
Cicalese, Ferdinando & Eduardo Sany Laber. (2006). On the competitive ratio of evaluating priced functions. Symposium on Discrete Algorithms. 53. 944–953. 2 indexed citations
18.
Cicalese, Ferdinando, Luisa Gargano, & Ugo Vaccaro. (2004). On searching strategies, parallel questions, and delayed answers. Discrete Applied Mathematics. 144(3). 247–262. 1 indexed citations
19.
Cicalese, Ferdinando, Daniele Mundici, & Ugo Vaccaro. (2002). Least adaptive optimal search with unreliable tests. Theoretical Computer Science. 270(1-2). 877–893. 12 indexed citations
20.
Cicalese, Ferdinando & Daniele Mundici. (2000). Perfect Two-Fault Tolerant Search with Minimum Adaptiveness. Advances in Applied Mathematics. 25(1). 65–101. 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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