Jay Whang
Impact in
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- Speech and Audio Processing
- Blind Source Separation Techniques
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- Image and Signal Denoising Methods
Papers in
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- Gaussian Processes and Bayesian Inference 3
- Evolutionary Algorithms and Applications 1
- Neural Networks and Applications 1
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- Image and Signal Denoising Methods 2
- Co-authors
- Alexandros G. Dimakis (6 shared papers)Nir Shlezinger (2 shared papers)Yonina C. Eldar (2 shared papers)Hyeji Kim (1 shared paper)Emma Brunskill (1 shared paper)Raphael Gontijo Lopes (1 shared paper)Chitwan Saharia (1 shared paper)Saurabh Saxena (1 shared paper)
- Journals
- Proceedings of the IEEE (1 paper)arXiv (Cornell University) (2 papers)IEEE Journal on Selected Areas in Information Theory (1 paper)
- Partner nations
- United StatesIsrael
In The Last Decade
Jay Whang
8 papers receiving 203 citations
Jay Whang's Hit Papers
Peers
Comparison fields: 5 of 67
- Signal Processing 39
- Computer Vision and Pattern Recognition 41
- Artificial Intelligence 62
- Aerospace Engineering 33
- Media Technology 9
Countries citing papers authored by Jay Whang
This map shows the geographic impact of Jay Whang'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 Jay Whang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay Whang more than expected).
Fields of papers citing papers by Jay Whang
This network shows the impact of papers produced by Jay Whang. 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 Jay Whang. The network helps show where Jay Whang may publish in the future.
Co-authors
The 13 scholars most cited alongside Jay Whang, 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 | Model-Based Deep Learning Hit paper breakdown → | 2023 | 167 |
| 2 | 2021 | 25 | |
| 3 | 2024 | 3 | |
| 4 | Strategic Object Oriented Reinforcement Learning. | 2018 | 3 |
| 5 | 2022 | 2 | |
| 6 | Compressed Sensing with Invertible Generative Models and Dependent Noise | 2020 | 2 |
| 7 | 2020 | 2 | |
| 8 | 2020 | 1 |
About Jay Whang
Jay Whang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Mechanics and Management Science and Operations Research, having authored 8 papers that have together received 205 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (3 papers), Model Reduction and Neural Networks (3 papers), Image and Signal Denoising Methods (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Cell Image Analysis Techniques (1 paper), Evolutionary Algorithms and Applications (1 paper), Neural Networks and Applications (1 paper) and Photoacoustic and Ultrasonic Imaging (1 paper). The work is most often cited by research in Signal Processing (39 citations), Computer Vision and Pattern Recognition (41 citations), Artificial Intelligence (62 citations), Aerospace Engineering (33 citations) and Media Technology (9 citations). Jay Whang has collaborated with scholars based in United States and Israel. Frequent co-authors include Alexandros G. Dimakis, Nir Shlezinger, Yonina C. Eldar, Hyeji Kim, Emma Brunskill, Raphael Gontijo Lopes, Chitwan Saharia, Saurabh Saxena, Qi Lei and David J. Fleet. Their work appears in journals such as Proceedings of the IEEE, arXiv (Cornell University) and IEEE Journal on Selected Areas in Information Theory.
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.