Pascal Sturmfels
Impact in
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Machine Learning in Healthcare
Papers in
-
- Explainable Artificial Intelligence (XAI) 2
- Adversarial Robustness in Machine Learning 1
- Machine Learning in Healthcare 1
- Topic Modeling 1
-
- Data Visualization and Analytics 1
- Co-authors
- Su‐In Lee (3 shared papers)Scott Lundberg (2 shared papers)Gabriel Erion (1 shared paper)Joseph D. Janizek (1 shared paper)Saige Rutherford (2 shared papers)Mike Angstadt (2 shared papers)Chandra Sripada (2 shared papers)Jenna Wiens (2 shared papers)
- Journals
- Neuroinformatics (1 paper)Nature Communications (1 paper)BMC Bioinformatics (1 paper)Lecture notes in computer science (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)
- Partner nations
- United StatesNetherlandsIceland
In The Last Decade
Pascal Sturmfels
8 papers receiving 200 citations
Peers
Comparison fields: 5 of 72
- Health Informatics 8
- Artificial Intelligence 109
- Biophysics 10
- Computer Vision and Pattern Recognition 33
- Pediatrics, Perinatology and Child Health 25
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-authors
The 24 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 | 2020 | 110 | |
| 2 | 2021 | 31 | |
| 3 | Learning Explainable Models Using Attribution Priors | 2019 | 25 |
| 4 | 2021 | 14 | |
| 5 | 2016 | 10 | |
| 6 | 2021 | 8 | |
| 7 | 2018 | 6 | |
| 8 | A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images | 2018 | 2 |
About Pascal Sturmfels
Pascal Sturmfels is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Computational Theory and Mathematics and Computer Networks and Communications, having authored 8 papers that have together received 206 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (2 papers), Fetal and Pediatric Neurological Disorders (1 paper), Gene expression and cancer classification (1 paper), Adversarial Robustness in Machine Learning (1 paper), Data Visualization and Analytics (1 paper), Bioinformatics and Genomic Networks (1 paper), Machine Learning in Healthcare (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Health Informatics (8 citations), Artificial Intelligence (109 citations), Biophysics (10 citations), Computer Vision and Pattern Recognition (33 citations) and Pediatrics, Perinatology and Child Health (25 citations). Pascal Sturmfels has collaborated with scholars based in United States, Netherlands and Iceland. Frequent co-authors include Su‐In Lee, Scott Lundberg, Gabriel Erion, Joseph D. Janizek, Saige Rutherford, Mike Angstadt, Chandra Sripada, Jenna Wiens, Ivan Evtimov and Dustin Scheinost. Their work appears in journals such as Neuroinformatics, Nature Communications, BMC Bioinformatics, Lecture notes in computer science and DOAJ (DOAJ: Directory of Open Access Journals).
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.