Jeremiah Neubert
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Artificial Intelligence
- Neurology
- Biomedical Engineering
- Co-authors
- Intisar Rizwan I HaqueNicola FerrierJohn PretloveTom DrummondWilliam SemkeTamanna T. K. MuniaReza Fazel-RezaiNaima Kaabouch
- Topics
- Experimental Learning in Engineering (10 papers)Engineering Education and Pedagogy (10 papers)Engineering Education and Curriculum Development (8 papers)
- Journals
- Journal of Intelligent & Robotic SystemsMachine Vision and ApplicationsInformatics in Medicine Unlocked
- Partner nations
- United StatesUnited KingdomSouth Sudan
In The Last Decade
Jeremiah Neubert
31 papers receiving 386 citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Computer Vision and Pattern Recognition 136
- Radiology, Nuclear Medicine and Imaging 134
- Artificial Intelligence 88
- Neurology 45
- Biomedical Engineering 43
Countries citing papers authored by Jeremiah Neubert
This map shows the geographic impact of Jeremiah Neubert'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 Jeremiah Neubert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeremiah Neubert more than expected).
Fields of papers citing papers by Jeremiah Neubert
This network shows the impact of papers produced by Jeremiah Neubert. 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 Jeremiah Neubert. The network helps show where Jeremiah Neubert may publish in the future.
Co-authorship network of co-authors of Jeremiah Neubert
This figure shows the co-authorship network connecting the top 25 collaborators of Jeremiah Neubert. A scholar is included among the top collaborators of Jeremiah Neubert 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 Jeremiah Neubert. Jeremiah Neubert 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 | 1 | |
| 3 | 0 | |
| 4 | Deep learning approaches to biomedical image segmentationbreakdown → | 277 |
| 5 | 4 | |
| 6 | 15 | |
| 7 | 1 | |
| 8 | Nano SSP Satellite | 1 |
| 9 | 12 | |
| 10 | 9 | |
| 11 | 1 | |
| 12 | 2 | |
| 13 | 1 | |
| 14 | 5 | |
| 15 | 15 | |
| 16 | 12 | |
| 17 | 5 | |
| 18 | 6 | |
| 19 | 4 | |
| 20 | Remediation of Scattered Light in near MSI Images | 1 |
About Jeremiah Neubert
Jeremiah Neubert is a scholar working on Architecture, Media Technology and Acoustics and Ultrasonics, having authored 36 papers that have together received 410 indexed citations. Recurring topics across this work include Experimental Learning in Engineering (10 papers), Engineering Education and Pedagogy (10 papers) and Engineering Education and Curriculum Development (8 papers). The work is most often cited by research in Health Informatics (11 citations), Architecture (12 citations) and Computer Vision and Pattern Recognition (136 citations). Jeremiah Neubert has collaborated with scholars based in United States, United Kingdom and South Sudan. Frequent co-authors include Intisar Rizwan I Haque, Nicola Ferrier, John Pretlove, Tom Drummond, William Semke, Tamanna T. K. Munia, Reza Fazel-Rezai, Naima Kaabouch, James A. Johnson and Yu Hen Hu. Their work appears in journals such as Journal of Intelligent & Robotic Systems, Machine Vision and Applications and Informatics in Medicine Unlocked.
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.