Eric Kernfeld
Impact in
- Computational Mathematics top 1%
- Tensor decomposition and applications
- Computational Mechanics top 10%
- Sparse and Compressive Sensing Techniques
Papers in
-
- Single-cell and spatial transcriptomics 4
- Pluripotent Stem Cells Research 2
-
- Invertebrate Immune Response Mechanisms 1
- Co-authors
- Shuchin Aeron (1 shared paper)Misha E. Kilmer (1 shared paper)René Maehr (5 shared papers)Ryan M. Genga (4 shared papers)Ping Xu (1 shared paper)Krishna Mohan Parsi (2 shared papers)Michael J. Ziller (2 shared papers)Fabian J. Theis (2 shared papers)
- Journals
- Genes Brain & Behavior (1 paper)Nature Communications (1 paper)Development (1 paper)Immunity (1 paper)Genome biology (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Eric Kernfeld
11 papers receiving 527 citations
Peers
Comparison fields: 5 of 87
- Computational Mathematics 136
- Computational Mechanics 112
- Biophysics 31
- Computer Vision and Pattern Recognition 84
- Immunology 84
Countries citing papers authored by Eric Kernfeld
This map shows the geographic impact of Eric Kernfeld'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 Eric Kernfeld with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Kernfeld more than expected).
Fields of papers citing papers by Eric Kernfeld
This network shows the impact of papers produced by Eric Kernfeld. 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 Eric Kernfeld. The network helps show where Eric Kernfeld may publish in the future.
Co-authors
The 25 scholars most cited alongside Eric Kernfeld, 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 | 2015 | 191 | |
| 2 | 2018 | 128 | |
| 3 | 2019 | 77 | |
| 4 | 2019 | 63 | |
| 5 | 2022 | 26 | |
| 6 | 2016 | 20 | |
| 7 | 2017 | 8 | |
| 8 | 2024 | 6 | |
| 9 | 2024 | 5 | |
| 10 | 2025 | 3 | |
| 11 | 2023 | 3 |
About Eric Kernfeld
Eric Kernfeld is a scholar working on Molecular Biology, Immunology, Surgery, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 530 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (4 papers), Pluripotent Stem Cells Research (2 papers), Invertebrate Immune Response Mechanisms (1 paper), Energy Harvesting in Wireless Networks (1 paper), Neurological diseases and metabolism (1 paper), Natural Language Processing Techniques (1 paper), Multimodal Machine Learning Applications (1 paper) and Tensor decomposition and applications (1 paper). The work is most often cited by research in Computational Mathematics (136 citations), Computational Mechanics (112 citations), Biophysics (31 citations), Computer Vision and Pattern Recognition (84 citations) and Immunology (84 citations). Eric Kernfeld has collaborated with scholars based in United States and Germany. Frequent co-authors include Shuchin Aeron, Misha E. Kilmer, René Maehr, Ryan M. Genga, Ping Xu, Krishna Mohan Parsi, Michael J. Ziller, Fabian J. Theis, David S. Fischer and Jan Hasenauer. Their work appears in journals such as Genes Brain & Behavior, Nature Communications, Development, Immunity and Genome biology.
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.