Russell Greiner
Impact in
- Artificial Intelligence top 0.5%
- Bayesian Modeling and Causal Inference
- Machine Learning and Algorithms
- Machine Learning and Data Classification
- Health Informatics top 2%
Papers in
-
- Machine Learning and Algorithms 37
- Machine Learning and Data Classification 29
- Bayesian Modeling and Causal Inference 20
- AI-based Problem Solving and Planning 16
- Machine Learning in Healthcare 14
- Co-authors
- David S. WishartJie ChengRoman EisnerYannick Djoumbou-FeunangMatthew BrownDuane SzafronDavid BellWeiru Liu
- Journals
- Artificial Intelligence (13 papers)PLoS ONE (10 papers)Nucleic Acids Research (6 papers)Scientific Reports (6 papers)Analytical Chemistry (4 papers)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Russell Greiner
245 papers receiving 8.8k citations
Hit Papers
Peers
Comparison fields: 5 of 207
- Artificial Intelligence 2.4k
- Health Informatics 81
- Computational Theory and Mathematics 887
- Software 184
- Molecular Biology 3.2k
Countries citing papers authored by Russell Greiner
This map shows the geographic impact of Russell Greiner'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 Russell Greiner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Russell Greiner more than expected).
Fields of papers citing papers by Russell Greiner
This network shows the impact of papers produced by Russell Greiner. 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 Russell Greiner. The network helps show where Russell Greiner may publish in the future.
Co-authors
The 25 scholars most cited alongside Russell Greiner, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 5 | |
| 6 | 2023 | 19 | |
| 7 | 2023 | 14 | |
| 8 | 2023 | 6 | |
| 9 | 2023 | 2 | |
| 10 | 2022 | 21 | |
| 11 | 2022 | 6 | |
| 12 | 2022 | 3 | |
| 13 | 2021 | 55 | |
| 14 | 2021 | 28 | |
| 15 | 2021 | 3 | |
| 16 | 2020 | 57 | |
| 17 | 2019 | 12 | |
| 18 | Constrained classification on structured data | 2008 | 0 |
| 19 | Why Experimentation can be better than Perfect Guidance | 1997 | 5 |
| 20 | Probably approximately optimal derivation strategies | 1991 | 10 |
About Russell Greiner
Russell Greiner is a scholar working on Artificial Intelligence, Modeling and Simulation, Health Informatics, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 263 papers that have together received 9.2k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (37 papers), Machine Learning and Data Classification (29 papers), Bayesian Modeling and Causal Inference (20 papers), Metabolomics and Mass Spectrometry Studies (19 papers), Functional Brain Connectivity Studies (17 papers), AI-based Problem Solving and Planning (16 papers), Bioinformatics and Genomic Networks (14 papers) and Machine Learning in Healthcare (14 papers). The work is most often cited by research in Artificial Intelligence (2.4k citations), Health Informatics (81 citations), Computational Theory and Mathematics (887 citations), Software (184 citations) and Molecular Biology (3.2k citations). Russell Greiner has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include David S. Wishart, Jie Cheng, Roman Eisner, Yannick Djoumbou-Feunang, Matthew Brown, Duane Szafron, David Bell, Weiru Liu, Jonathan Kelly and Felicity Allen. Their work appears in journals such as Artificial Intelligence, PLoS ONE, Nucleic Acids Research, Scientific Reports and Analytical Chemistry.
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.