Luca Biggio

8 papers receiving 213 citations

Luca Biggio's Hit Papers

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial 2023 · 103 citations
1030+1+2Years since publication255075100

Peers

Luca Biggio
Comparison fields: 5 of 73
  • Control and Systems Engineering 103
  • Medical Laboratory Technology 6
  • Statistics, Probability and Uncertainty 21
  • Safety, Risk, Reliability and Quality 26
  • Automotive Engineering 31
Replace Kavindu Ranasinghe with:
Kavindu Ranasinghe Australia
Linyu Lin United States
Xisheng Jia China
Ioanna Aslanidou Sweden
David Celeita Colombia
Ana María Peco Chacón Spain
Rachid Nait-Said Algeria
U. Grasselli Italy
Dong-Sik Gu South Korea
Luca Biggio relative to Kavindu Ranasinghe Australia Kavindu Ranasinghe's profile →
Citations per field
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Kavindu Ranasinghe · 1×
Citations per year

Countries citing papers authored by Luca Biggio

Since Specialization
Citations

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

Fields of papers citing papers by Luca Biggio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 20 scholars most cited alongside Luca Biggio, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Luca Biggio Line = papers co-authored together Luca Biggio links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
Hit paper breakdown →
2023103
2 202053
3 202130
4 202321
5 20235
6 20244
7 20233
8 20243

About Luca Biggio

Luca Biggio is a scholar working on Control and Systems Engineering, Artificial Intelligence, Instrumentation, Computational Mechanics and Automotive Engineering, having authored 8 papers that have together received 222 indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (2 papers), Fault Detection and Control Systems (2 papers), Astronomical Observations and Instrumentation (2 papers), Astronomy and Astrophysical Research (2 papers), Time Series Analysis and Forecasting (1 paper), Advanced Battery Materials and Technologies (1 paper), Industrial Vision Systems and Defect Detection (1 paper) and Probabilistic and Robust Engineering Design (1 paper). The work is most often cited by research in Control and Systems Engineering (103 citations), Medical Laboratory Technology (6 citations), Statistics, Probability and Uncertainty (21 citations), Safety, Risk, Reliability and Quality (26 citations) and Automotive Engineering (31 citations). Luca Biggio has collaborated with scholars based in Switzerland, United States and Taiwan. Frequent co-authors include Olga Fink, Venkat Pavan Nemani, Yan Wang, Chao Hu, Zhen Hu, Xiaoge Zhang, Xun Huan, Anh Tran, Manuel Arias Chao and Chetan S. Kulkarni. Their work appears in journals such as Applied Energy, IEEE Access, Monthly Notices of the Royal Astronomical Society, Astronomy and Astrophysics and Mechanical Systems and Signal Processing.

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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