Brian Dolhansky
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Signal Processing
- Cognitive Neuroscience
- Safety Research
- Co-authors
- Joanna BittonMenglin WangCristian Canton-FerrerCaner HazırbaşAlbert GordoJacqueline PanJeff BilmesCristian Canton Ferrer
- Topics
- Adversarial Robustness in Machine Learning (3 papers)Generative Adversarial Networks and Image Synthesis (2 papers)Music Technology and Sound Studies (2 papers)
- Journals
- IEEE Transactions on Biometrics Behavior and Identity SciencearXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- United StatesIsrael
In The Last Decade
Brian Dolhansky
8 papers receiving 196 citations
Peers
Comparison fields: 5 of 46
- Computer Vision and Pattern Recognition 150
- Artificial Intelligence 73
- Signal Processing 28
- Cognitive Neuroscience 11
- Safety Research 11
Countries citing papers authored by Brian Dolhansky
This map shows the geographic impact of Brian Dolhansky'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 Brian Dolhansky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Dolhansky more than expected).
Fields of papers citing papers by Brian Dolhansky
This network shows the impact of papers produced by Brian Dolhansky. 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 Brian Dolhansky. The network helps show where Brian Dolhansky may publish in the future.
Co-authorship network of co-authors of Brian Dolhansky
This figure shows the co-authorship network connecting the top 25 collaborators of Brian Dolhansky. A scholar is included among the top collaborators of Brian Dolhansky based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Brian Dolhansky. Brian Dolhansky is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 16 | |
| 3 | 8 | |
| 4 | 40 | |
| 5 | The DeepFake Detection Challenge Dataset | 120 |
| 6 | Deep Submodular Functions: Definitions and Learning | 9 |
| 7 | Relating Perceptual and Feature Space Invariances in Music Emotion Recognition | 3 |
| 8 | 12 |
About Brian Dolhansky
Brian Dolhansky is a scholar working on Architecture, Computer Vision and Pattern Recognition and Safety Research, having authored 8 papers that have together received 211 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Music Technology and Sound Studies (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (150 citations), Signal Processing (28 citations) and Artificial Intelligence (73 citations). Brian Dolhansky has collaborated with scholars based in United States and Israel. Frequent co-authors include Joanna Bitton, Menglin Wang, Cristian Canton-Ferrer, Caner Hazırbaş, Albert Gordo, Jacqueline Pan, Jeff Bilmes, Cristian Canton Ferrer, Farinaz Koushanfar and Erik M. Schmidt. Their work appears in journals such as IEEE Transactions on Biometrics Behavior and Identity Science, 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.