Pascal Sturmfels
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- Explainable Artificial Intelligence (XAI) 2
- Machine Learning and Data Classification 1
- Machine Learning in Healthcare 1
- Privacy-Preserving Technologies in Data 1
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- Neonatal and fetal brain pathology 1
- Fetal and Pediatric Neurological Disorders 1
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- RNA modifications and cancer 1
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- Computational Drug Discovery Methods 1
- Co-authors
- Su‐In LeeScott LundbergGabriel ErionJoseph D. JanizekMarion I. van den HeuvelChandra SripadaMike AngstadtJasmine L. Hect
- Partner nations
- United StatesNetherlandsCanada
In The Last Decade
Pascal Sturmfels
7 papers receiving 163 citations
Peers
Comparison fields: 5 of 67
- Health Informatics 8
- Artificial Intelligence 90
- Computer Vision and Pattern Recognition 26
- Biophysics 7
- Pediatrics, Perinatology and Child Health 24
Countries citing papers authored by Pascal Sturmfels
This map shows the geographic impact of Pascal Sturmfels'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 Pascal Sturmfels with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pascal Sturmfels more than expected).
Fields of papers citing papers by Pascal Sturmfels
This network shows the impact of papers produced by Pascal Sturmfels. 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 Pascal Sturmfels. The network helps show where Pascal Sturmfels may publish in the future.
Co-authorship network
The 22 scholars most cited alongside Pascal Sturmfels, 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 | 2021 | 6 | |
| 2 | 2021 | 13 | |
| 3 | 2021 | 28 | |
| 4 | 2020 | 87 | |
| 5 | Learning Explainable Models Using Attribution Priors | 2019 | 22 |
| 6 | A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images | 2018 | 2 |
| 7 | 2016 | 10 |
About Pascal Sturmfels
Pascal Sturmfels is a scholar working on Biophysics, Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Theory and Mathematics and Cognitive Neuroscience, having authored 7 papers that have together received 168 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (2 papers), Machine Learning and Data Classification (1 paper), RNA modifications and cancer (1 paper), Neonatal and fetal brain pathology (1 paper), Fetal and Pediatric Neurological Disorders (1 paper), Machine Learning in Healthcare (1 paper), Privacy-Preserving Technologies in Data (1 paper) and Computational Drug Discovery Methods (1 paper). The work is most often cited by research in Health Informatics (8 citations), Artificial Intelligence (90 citations), Computer Vision and Pattern Recognition (26 citations), Biophysics (7 citations) and Pediatrics, Perinatology and Child Health (24 citations). Pascal Sturmfels has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Su‐In Lee, Scott Lundberg, Gabriel Erion, Joseph D. Janizek, Marion I. van den Heuvel, Chandra Sripada, Mike Angstadt, Jasmine L. Hect, Moriah E. Thomason and Saige Rutherford. Their work appears in journals such as Neuroinformatics, Nature Communications, BMC Bioinformatics and SHILAP Revista de lepidopterología.
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.