Corey Nolet
Impact in
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques
- Artificial Intelligence top 10%
- Artificial Intelligence Applications
Papers in
-
- Parallel Computing and Optimization Techniques 4
-
- Advanced Clustering Algorithms Research 1
- Computational Physics and Python Applications 1
- Algorithms and Data Compression 1
- Artificial Intelligence Applications 1
- Machine Learning and Data Classification 1
- Co-authors
- Joshua M. Patterson (2 shared papers)Sebastian Raschka (1 shared paper)T. Fehér (1 shared paper)Akira Naruse (1 shared paper)Edward Raff (2 shared papers)Tim Oates (2 shared papers)Joe Eaton (3 shared papers)Oded Green (2 shared papers)
- Journals
- Gastroenterology (1 paper)Information (1 paper)Lecture notes in computer science (1 paper)IEEE High Performance Extreme Computing Conference (1 paper)Proceedings - International Conference on Data Engineering (1 paper)
- Partner nations
- United StatesUgandaUnited Kingdom
In The Last Decade
Corey Nolet
6 papers receiving 513 citations
Corey Nolet's Hit Papers
Peers
Comparison fields: 5 of 115
- Hardware and Architecture 39
- Artificial Intelligence 129
- Computer Science Applications 16
- Information Systems 69
- Computer Vision and Pattern Recognition 58
Countries citing papers authored by Corey Nolet
This map shows the geographic impact of Corey Nolet'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 Corey Nolet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Corey Nolet more than expected).
Fields of papers citing papers by Corey Nolet
This network shows the impact of papers produced by Corey Nolet. 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 Corey Nolet. The network helps show where Corey Nolet may publish in the future.
Co-authors
The 9 scholars most cited alongside Corey Nolet, 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 | Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence Hit paper breakdown → | 2020 | 474 |
| 2 | 2024 | 23 | |
| 3 | 2021 | 20 | |
| 4 | 2022 | 11 | |
| 5 | 2023 | 4 | |
| 6 | 2022 | 2 | |
| 7 | 2025 | 0 |
About Corey Nolet
Corey Nolet is a scholar working on Hardware and Architecture, Artificial Intelligence, Computer Vision and Pattern Recognition, Surgery and Nuclear Energy and Engineering, having authored 7 papers that have together received 534 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (4 papers), Advanced Clustering Algorithms Research (1 paper), Advanced Image and Video Retrieval Techniques (1 paper), Computational Physics and Python Applications (1 paper), Algorithms and Data Compression (1 paper), Esophageal Cancer Research and Treatment (1 paper), Artificial Intelligence Applications (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Hardware and Architecture (39 citations), Artificial Intelligence (129 citations), Computer Science Applications (16 citations), Information Systems (69 citations) and Computer Vision and Pattern Recognition (58 citations). Corey Nolet has collaborated with scholars based in United States, Uganda and United Kingdom. Frequent co-authors include Joshua M. Patterson, Sebastian Raschka, T. Fehér, Akira Naruse, Edward Raff, Tim Oates, Joe Eaton, Oded Green and Justin Luitjens. Their work appears in journals such as Gastroenterology, Information, Lecture notes in computer science, IEEE High Performance Extreme Computing Conference and Proceedings - International Conference on Data Engineering.
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.