Jeff Pool
Impact in
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques
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- Advanced Neural Network Applications
- Advanced Vision and Imaging
- Advanced Image and Video Retrieval Techniques
Papers in
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- Parallel Computing and Optimization Techniques 6
- Embedded Systems Design Techniques 2
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- Advanced Neural Network Applications 2
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Anselmo Lastra (5 shared papers)Montek Singh (5 shared papers)Wenshuo Li (1 shared paper)Yu Wang (1 shared paper)William J. Dally (1 shared paper)Xingyu Liu (1 shared paper)Huizi Mao (1 shared paper)Song Han (1 shared paper)
- Journals
- Journal of Low Power Electronics (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (2 papers)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Jeff Pool
10 papers receiving 231 citations
Peers
Comparison fields: 5 of 39
- Hardware and Architecture 54
- Computer Vision and Pattern Recognition 154
- Computer Graphics and Computer-Aided Design 19
- Artificial Intelligence 85
- Signal Processing 25
Countries citing papers authored by Jeff Pool
This map shows the geographic impact of Jeff Pool'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 Jeff Pool with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Pool more than expected).
Fields of papers citing papers by Jeff Pool
This network shows the impact of papers produced by Jeff Pool. 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 Jeff Pool. The network helps show where Jeff Pool may publish in the future.
Co-authors
The 17 scholars most cited alongside Jeff Pool, 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 | 2017 | 146 | |
| 2 | 2008 | 26 | |
| 3 | 2010 | 20 | |
| 4 | 2011 | 18 | |
| 5 | 2018 | 10 | |
| 6 | 2012 | 8 | |
| 7 | 2011 | 8 | |
| 8 | Channel Permutations for N:M Sparsity | 2021 | 6 |
| 9 | Self-Supervised Generative Adversarial Compression. | 2020 | 5 |
| 10 | 2024 | 3 |
About Jeff Pool
Jeff Pool is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Computational Theory and Mathematics and Artificial Intelligence, having authored 10 papers that have together received 250 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (6 papers), Low-power high-performance VLSI design (3 papers), Advanced Data Storage Technologies (2 papers), Embedded Systems Design Techniques (2 papers), Ferroelectric and Negative Capacitance Devices (2 papers), Advanced Neural Network Applications (2 papers), Generative Adversarial Networks and Image Synthesis (1 paper) and graph theory and CDMA systems (1 paper). The work is most often cited by research in Hardware and Architecture (54 citations), Computer Vision and Pattern Recognition (154 citations), Computer Graphics and Computer-Aided Design (19 citations), Artificial Intelligence (85 citations) and Signal Processing (25 citations). Jeff Pool has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Anselmo Lastra, Montek Singh, Wenshuo Li, Yu Wang, William J. Dally, Xingyu Liu, Huizi Mao, Song Han, Chong Yu and Minsoo Rhu. Their work appears in journals such as Journal of Low Power Electronics, arXiv (Cornell University) and Neural Information Processing Systems.
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.