Gregory Plumb
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Machine Learning in Healthcare
- Imbalanced Data Classification Techniques
Papers in
-
- Explainable Artificial Intelligence (XAI) 4
- Machine Learning and Data Classification 3
- Adversarial Robustness in Machine Learning 2
- Bayesian Methods and Mixture Models 1
-
- Experimental Learning in Engineering 2
- Co-authors
- Ameet Talwalkar (4 shared papers)Joon Sik Kim (2 shared papers)Valerie Chen (3 shared papers)Jeffrey Li (2 shared papers)Ángel Alexander Cabrera (1 shared paper)Vikas Singh (2 shared papers)Firoz Alam (1 shared paper)Risi Kondor (1 shared paper)
- Journals
- Queue (1 paper)Communications of the ACM (1 paper)Journal of Machine Learning Research (1 paper)Lecture notes in computer science (1 paper)IGI Global eBooks (1 paper)
- Partner nations
- United StatesAustralia
In The Last Decade
Gregory Plumb
8 papers receiving 536 citations
Gregory Plumb's Hit Papers
Peers
Comparison fields: 5 of 127
- Health Informatics 18
- Artificial Intelligence 265
- Safety Research 26
- Health Information Management 13
- Management Science and Operations Research 36
Countries citing papers authored by Gregory Plumb
This map shows the geographic impact of Gregory Plumb'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 Gregory Plumb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gregory Plumb more than expected).
Fields of papers citing papers by Gregory Plumb
This network shows the impact of papers produced by Gregory Plumb. 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 Gregory Plumb. The network helps show where Gregory Plumb may publish in the future.
Co-authors
The 15 scholars most cited alongside Gregory Plumb, 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 | Interpretable Machine Learning Hit paper breakdown → | 2021 | 401 |
| 2 | 2022 | 148 | |
| 3 | 2023 | 9 | |
| 4 | S n FFT: a Julia toolkit for Fourier analysis of functions over permutations | 2015 | 4 |
| 5 | 2012 | 3 | |
| 6 | 2022 | 1 | |
| 7 | 2014 | 1 | |
| 8 | 2020 | 1 | |
| 9 | 2017 | 0 |
About Gregory Plumb
Gregory Plumb is a scholar working on Artificial Intelligence, Media Technology, Computational Mathematics, Architecture and Biophysics, having authored 9 papers that have together received 568 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (4 papers), Machine Learning and Data Classification (3 papers), Adversarial Robustness in Machine Learning (2 papers), Experimental Learning in Engineering (2 papers), Advanced Data Compression Techniques (1 paper), Bayesian Methods and Mixture Models (1 paper), Online and Blended Learning (1 paper) and Mobile Learning in Education (1 paper). The work is most often cited by research in Health Informatics (18 citations), Artificial Intelligence (265 citations), Safety Research (26 citations), Health Information Management (13 citations) and Management Science and Operations Research (36 citations). Gregory Plumb has collaborated with scholars based in United States and Australia. Frequent co-authors include Ameet Talwalkar, Joon Sik Kim, Valerie Chen, Jeffrey Li, Ángel Alexander Cabrera, Vikas Singh, Firoz Alam, Risi Kondor, Mark R. Shortis and Aleksandar Subic. Their work appears in journals such as Queue, Communications of the ACM, Journal of Machine Learning Research, Lecture notes in computer science and IGI Global eBooks.
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.