Battista Biggio
- Artificial Intelligence top 0.5%
- Signal Processing top 0.2%
- Computer Networks and Communications top 1%
- Information Systems top 1%
- Computer Vision and Pattern Recognition top 2%
- Co-authors
- Fabio RoliGiorgio FumeraAlina OpreaAmbra DemontisBlaine NelsonCristina Nita-RotaruBo LiChang Liu
- Topics
- Adversarial Robustness in Machine Learning (59 papers)Advanced Malware Detection Techniques (41 papers)Anomaly Detection Techniques and Applications (27 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceACM Computing SurveysPattern Recognition
- Partner nations
- ItalyGermanySwitzerland
In The Last Decade
Battista Biggio
85 papers receiving 3.2k citations
Hit Papers
Peers
Comparison fields: 5 of 113
- Artificial Intelligence 2.5k
- Signal Processing 1.5k
- Computer Networks and Communications 963
- Information Systems 671
- Computer Vision and Pattern Recognition 476
Countries citing papers authored by Battista Biggio
This map shows the geographic impact of Battista Biggio'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 Battista Biggio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Battista Biggio more than expected).
Fields of papers citing papers by Battista Biggio
This network shows the impact of papers produced by Battista Biggio. 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 Battista Biggio. The network helps show where Battista Biggio may publish in the future.
Co-authorship network of co-authors of Battista Biggio
This figure shows the co-authorship network connecting the top 25 collaborators of Battista Biggio. A scholar is included among the top collaborators of Battista Biggio 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 Battista Biggio. Battista Biggio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 12 | |
| 6 | 12 | |
| 7 | 15 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 4 | |
| 11 | 22 | |
| 12 | 1 | |
| 13 | 9 | |
| 14 | 8 | |
| 15 | 9 | |
| 16 | 2 | |
| 17 | 21 | |
| 18 | 11 | |
| 19 | 44 | |
| 20 | 40 |
About Battista Biggio
Battista Biggio is a scholar working on Signal Processing, Artificial Intelligence and Information Systems, having authored 97 papers that have together received 3.3k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (59 papers), Advanced Malware Detection Techniques (41 papers) and Anomaly Detection Techniques and Applications (27 papers). The work is most often cited by research in Signal Processing (1.5k citations), Artificial Intelligence (2.5k citations) and Computer Networks and Communications (963 citations). Battista Biggio has collaborated with scholars based in Italy, Germany and Switzerland. Frequent co-authors include Fabio Roli, Giorgio Fumera, Alina Oprea, Ambra Demontis, Blaine Nelson, Cristina Nita-Rotaru, Bo Li, Chang Liu, Matthew Jagielski and Giorgio Giacinto. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Computing Surveys and Pattern Recognition.
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.