Jeff Johnson
Impact in
- Parasitology top 2%
- Toxoplasma gondii Research Studies
- Parasitic Infections and Diagnostics
- Spectroscopy top 10%
- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
Papers in
-
- Geochemistry and Geologic Mapping 2
- Cryptography and Data Security 1
- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- John R. Yates (3 shared papers)Johannes Graumann (1 shared paper)Daniel Cociorva (1 shared paper)Michael J. MacCoss (1 shared paper)David L. Tabb (1 shared paper)W. Hayes McDonald (1 shared paper)Rovshan G. Sadygov (1 shared paper)John D. Venable (1 shared paper)
- Journals
- PROTEOMICS (1 paper)Rapid Communications in Mass Spectrometry (1 paper)PLoS Pathogens (1 paper)IEEE Transactions on Big Data (1 paper)LPI (2 papers)
- Partner nations
- United StatesGermanyAustria
In The Last Decade
Jeff Johnson
10 papers receiving 649 citations
Jeff Johnson's Hit Papers
Peers
Comparison fields: 5 of 90
- Parasitology 207
- Spectroscopy 122
- Virology 20
- Molecular Biology 288
- Cell Biology 67
Countries citing papers authored by Jeff Johnson
This map shows the geographic impact of Jeff Johnson'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 Jeff Johnson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Johnson more than expected).
Fields of papers citing papers by Jeff Johnson
This network shows the impact of papers produced by Jeff Johnson. 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 Jeff Johnson. The network helps show where Jeff Johnson may publish in the future.
Co-authors
The 25 scholars most cited alongside Jeff Johnson, 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 | 2004 | 306 | |
| 2 | 2006 | 220 | |
| 3 | The Faiss Library Hit paper breakdown → | 2025 | 62 |
| 4 | 2008 | 30 | |
| 5 | Analysis of Mars Thermal Emission Spectrometer Data Using Large Mineral Reference Libraries | 2004 | 10 |
| 6 | 2024 | 7 | |
| 7 | 2002 | 7 | |
| 8 | 2024 | 5 | |
| 9 | Mineral Mapping in Valles Marineris, Mars: A New Approach to Spectral Demixing of TES Data | 2003 | 2 |
| 10 | 2017 | 2 |
About Jeff Johnson
Jeff Johnson is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Virology and Mechanics of Materials, having authored 10 papers that have together received 651 indexed citations. Recurring topics across this work include Geochemistry and Geologic Mapping (2 papers), Invertebrate Immune Response Mechanisms (1 paper), Advanced Proteomics Techniques and Applications (1 paper), Toxoplasma gondii Research Studies (1 paper), Numerical Methods and Algorithms (1 paper), Digital Filter Design and Implementation (1 paper), Cryptography and Data Security (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Parasitology (207 citations), Spectroscopy (122 citations), Virology (20 citations), Molecular Biology (288 citations) and Cell Biology (67 citations). Jeff Johnson has collaborated with scholars based in United States, Germany and Austria. Frequent co-authors include John R. Yates, Johannes Graumann, Daniel Cociorva, Michael J. MacCoss, David L. Tabb, W. Hayes McDonald, Rovshan G. Sadygov, John D. Venable, Sapna Suravajjala and David S. Roos. Their work appears in journals such as PROTEOMICS, Rapid Communications in Mass Spectrometry, PLoS Pathogens, IEEE Transactions on Big Data and LPI.
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.