Eugene Brevdo
Impact in
- Conservation top 2%
-
- Image and Signal Denoising Methods
- Image Retrieval and Classification Techniques
Papers in
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- Image and Signal Denoising Methods 3
- Image Retrieval and Classification Techniques 3
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- Adversarial Robustness in Machine Learning 1
- Reinforcement Learning in Robotics 1
- Co-authors
- Neven S. Fučkar (2 shared papers)Gaurav Thakur (2 shared papers)Hau‐Tieng Wu (2 shared papers)Ingrid Daubechies (3 shared papers)Shannon M. Hughes (3 shared papers)Eric Postma (1 shared paper)Jia Li (1 shared paper)Craig Johnson (1 shared paper)
- Journals
- IEEE Signal Processing Magazine (1 paper)Signal Processing (1 paper)Proceedings of the ACM on Management of Data (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesNetherlandsBelgium
In The Last Decade
Eugene Brevdo
10 papers receiving 667 citations
Eugene Brevdo's Hit Papers
Peers
Comparison fields: 5 of 105
- Conservation 40
- Computer Vision and Pattern Recognition 225
- Cognitive Neuroscience 189
- Control and Systems Engineering 208
- Signal Processing 95
Countries citing papers authored by Eugene Brevdo
This map shows the geographic impact of Eugene Brevdo'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 Eugene Brevdo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugene Brevdo more than expected).
Fields of papers citing papers by Eugene Brevdo
This network shows the impact of papers produced by Eugene Brevdo. 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 Eugene Brevdo. The network helps show where Eugene Brevdo may publish in the future.
Co-authors
The 25 scholars most cited alongside Eugene Brevdo, 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 | The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications Hit paper breakdown → | 2012 | 409 |
| 2 | 2008 | 195 | |
| 3 | Stylistic analysis of paintings usingwavelets and machine learning | 2009 | 21 |
| 4 | 2011 | 17 | |
| 5 | 2023 | 15 | |
| 6 | Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion | 2018 | 10 |
| 7 | Deep Probabilistic Programming | 2017 | 9 |
| 8 | 2011 | 9 | |
| 9 | 2024 | 3 | |
| 10 | 2009 | 1 | |
| 11 | 2006 | 1 |
About Eugene Brevdo
Eugene Brevdo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience, Civil and Structural Engineering and Statistical and Nonlinear Physics, having authored 11 papers that have together received 690 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (3 papers), Image Retrieval and Classification Techniques (3 papers), Aesthetic Perception and Analysis (3 papers), Structural Health Monitoring Techniques (2 papers), Scientific Research and Discoveries (1 paper), Advanced Database Systems and Queries (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Conservation (40 citations), Computer Vision and Pattern Recognition (225 citations), Cognitive Neuroscience (189 citations), Control and Systems Engineering (208 citations) and Signal Processing (95 citations). Eugene Brevdo has collaborated with scholars based in United States, Netherlands and Belgium. Frequent co-authors include Neven S. Fučkar, Gaurav Thakur, Hau‐Tieng Wu, Ingrid Daubechies, Shannon M. Hughes, Eric Postma, Jia Li, Craig Johnson, Ella Hendriks and James Z. Wang. Their work appears in journals such as IEEE Signal Processing Magazine, Signal Processing, Proceedings of the ACM on Management of Data, 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.