Brian Quanz
Impact in
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Text and Document Classification Technologies
- Anomaly Detection Techniques and Applications
Papers in
-
- Machine Learning and Data Classification 5
- Domain Adaptation and Few-Shot Learning 4
- Anomaly Detection Techniques and Applications 2
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- Gene expression and cancer classification 3
- Co-authors
- Jun Huan (3 shared papers)Costas Tsatsoulis (1 shared paper)Hongliang Fei (3 shared papers)Pavithra Harsha (4 shared papers)Jianbo Li (1 shared paper)Yada Zhu (1 shared paper)Ajay Deshpande (1 shared paper)Jingrui He (1 shared paper)
- Journals
- Manufacturing & Service Operations Management (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)Scientific Reports (1 paper)2022 IEEE International Conference on Big Data (Big Data) (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- United StatesIndiaIreland
In The Last Decade
Brian Quanz
20 papers receiving 275 citations
Peers
Comparison fields: 5 of 69
- Artificial Intelligence 172
- Computational Mathematics 3
- Signal Processing 42
- Computer Vision and Pattern Recognition 78
- Management Science and Operations Research 24
Countries citing papers authored by Brian Quanz
This map shows the geographic impact of Brian Quanz'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 Brian Quanz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Quanz more than expected).
Fields of papers citing papers by Brian Quanz
This network shows the impact of papers produced by Brian Quanz. 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 Brian Quanz. The network helps show where Brian Quanz may publish in the future.
Co-authors
The 25 scholars most cited alongside Brian Quanz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 104 | |
| 2 | 2012 | 48 | |
| 3 | 2021 | 40 | |
| 4 | 2011 | 17 | |
| 5 | 2018 | 10 | |
| 6 | 2009 | 8 | |
| 7 | 2010 | 7 | |
| 8 | 2008 | 7 | |
| 9 | 2008 | 7 | |
| 10 | 2023 | 6 | |
| 11 | 2009 | 5 | |
| 12 | 2012 | 5 | |
| 13 | 2021 | 3 | |
| 14 | 2022 | 3 | |
| 15 | 2023 | 3 | |
| 16 | 2025 | 2 | |
| 17 | 2025 | 2 | |
| 18 | 2022 | 2 | |
| 19 | 2009 | 1 | |
| 20 | 2024 | 1 |
About Brian Quanz
Brian Quanz is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Management Science and Operations Research and Computer Networks and Communications, having authored 20 papers that have together received 281 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Gene expression and cancer classification (3 papers), Face and Expression Recognition (3 papers), Artificial Immune Systems Applications (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Network Security and Intrusion Detection (2 papers). The work is most often cited by research in Artificial Intelligence (172 citations), Computational Mathematics (3 citations), Signal Processing (42 citations), Computer Vision and Pattern Recognition (78 citations) and Management Science and Operations Research (24 citations). Brian Quanz has collaborated with scholars based in United States, India and Ireland. Frequent co-authors include Jun Huan, Jun Huan, Jun Huan, Costas Tsatsoulis, Hongliang Fei, Pavithra Harsha, Jianbo Li, Yada Zhu, Ajay Deshpande and Jingrui He. Their work appears in journals such as Manufacturing & Service Operations Management, IEEE Transactions on Knowledge and Data Engineering, Scientific Reports, 2022 IEEE International Conference on Big Data (Big Data) and SSRN Electronic Journal.
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.