Danilo Pau

1.5k total citations
125 papers, 844 citations indexed

About

Danilo Pau is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Danilo Pau has authored 125 papers receiving a total of 844 indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Computer Vision and Pattern Recognition, 42 papers in Electrical and Electronic Engineering and 41 papers in Artificial Intelligence. Recurrent topics in Danilo Pau's work include Advanced Neural Network Applications (13 papers), Neural Networks and Applications (12 papers) and Anomaly Detection Techniques and Applications (11 papers). Danilo Pau is often cited by papers focused on Advanced Neural Network Applications (13 papers), Neural Networks and Applications (12 papers) and Anomaly Detection Techniques and Applications (11 papers). Danilo Pau collaborates with scholars based in Italy, Switzerland and Czechia. Danilo Pau's co-authors include Gian Domenico Licciardo, Luigi Di Benedetto, Giambattista Gruosso, Laura Falaschetti, Claudio Turchetti, Valeria Tomaselli, Antonio Chimienti, Marco Piastra, Emanuele Torti and Claudia Ferraris and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and Expert Systems with Applications.

In The Last Decade

Danilo Pau

111 papers receiving 812 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Danilo Pau Italy	15	316	206	188	158	112	125	844
Chunsheng Liu China	19	593 1.9×	190 0.9×	325 1.7×	156 1.0×	33 0.3×	107	1.2k
Gaurav Singal India	18	208 0.7×	176 0.9×	262 1.4×	446 2.8×	174 1.6×	64	974
Aris S. Lalos Greece	18	237 0.8×	454 2.2×	137 0.7×	368 2.3×	71 0.6×	132	1.2k
André Eugênio Lazzaretti Brazil	15	273 0.9×	378 1.8×	365 1.9×	131 0.8×	51 0.5×	99	1.1k
Xing Hu China	18	411 1.3×	65 0.3×	363 1.9×	197 1.2×	45 0.4×	63	958
Tiago Gomes Portugal	14	150 0.5×	330 1.6×	141 0.8×	367 2.3×	65 0.6×	60	962
A. Prasanth India	15	122 0.4×	206 1.0×	209 1.1×	370 2.3×	67 0.6×	36	840
Chunbo Xiu China	14	176 0.6×	227 1.1×	161 0.9×	141 0.9×	44 0.4×	77	784
Junbao Li China	17	471 1.5×	237 1.2×	194 1.0×	75 0.5×	46 0.4×	90	1.1k
Filbert H. Juwono Malaysia	15	87 0.3×	406 2.0×	121 0.6×	104 0.7×	99 0.9×	104	907

Countries citing papers authored by Danilo Pau

Since Specialization

Citations

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

Fields of papers citing papers by Danilo Pau

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Danilo Pau

This figure shows the co-authorship network connecting the top 25 collaborators of Danilo Pau. A scholar is included among the top collaborators of Danilo Pau 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 Pau. Danilo Pau is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Pau, Danilo, et al.. (2025). Transitioning from TinyML to Edge GenAI: A Review. Preprints.org. 1 indexed citations

2.

Pau, Danilo, et al.. (2025). Reviewing progresses on In-Sensor AI Computing. Microprocessors and Microsystems. 105156–105156.

3.

Pau, Danilo, et al.. (2025). Transitioning from TinyML to Edge GenAI: A Review. Big Data and Cognitive Computing. 9(3). 61–61. 2 indexed citations

4.

Pau, Danilo, et al.. (2024). Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite. Information. 15(11). 674–674. 1 indexed citations

5.

Pau, Danilo, et al.. (2024). Tiny Machine Learning for Dynamic Line Rating of the Overhead Lines. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 114. 1–6. 1 indexed citations

6.

Pau, Danilo, et al.. (2024). In-Sensor Learning for Pressure Self-Calibration. 1–6. 2 indexed citations

7.

Vollero, Luca, et al.. (2024). Impact of Interfering Factors on a Glucose Sensor Model. 1–6.

8.

Pau, Danilo, et al.. (2024). Learning Pressure Sensor Drifts From Zero Deployability Budget. IEEE Sensors Letters. 8(8). 1–4. 1 indexed citations

9.

Berta, Riccardo, et al.. (2024). Developing a TinyML Image Classifier in an Hour. IEEE Open Journal of the Industrial Electronics Society. 5. 946–960. 4 indexed citations

10.

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

11.

Pau, Danilo, et al.. (2024). Forward Learning of Large Language Models by Consumer Devices. Electronics. 13(2). 402–402. 4 indexed citations

12.

Arpaïa, Pasquale, Luca Capobianco, Antonio Espósito, et al.. (2024). Accurate Energy Measurements for Tiny Machine Learning Workloads. 831–836. 2 indexed citations

13.

Pau, Danilo, et al.. (2024). Deep Neural Quantization for Speech Detection of Parkinson Disease. 178–183. 1 indexed citations

14.

Davoli, Luca, et al.. (2023). Air Quality Estimation with Embedded AI-Based Prediction Algorithms. 87–92. 2 indexed citations

15.

Pau, Danilo, et al.. (2023). TinyRCE: Multipurpose Forward Learning for Resource Restricted Devices. IEEE Sensors Letters. 7(10). 1–4. 6 indexed citations

16.

Pau, Danilo, et al.. (2023). Tiny Machine Learning Zoo for Long-Term Compensation of Pressure Sensor Drifts. Electronics. 12(23). 4819–4819. 5 indexed citations

17.

Pau, Danilo, et al.. (2023). Field oriented control dataset of a 3-phase permanent magnet synchronous motor. Data in Brief. 47. 109002–109002. 4 indexed citations

18.

Candelieri, Antonio, et al.. (2022). AutoTinyML for microcontrollers: Dealing with black-box deployability. Expert Systems with Applications. 207. 117876–117876. 7 indexed citations

19.

Falaschetti, Laura, et al.. (2022). A CNN-based image detector for plant leaf diseases classification. HardwareX. 12. e00363–e00363. 46 indexed citations

20.

Pau, Danilo, et al.. (2021). Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks. SHILAP Revista de lepidopterología. 39. 107538–107538. 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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