Will Xiao
Impact in
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function
- Face Recognition and Perception
- Visual perception and processing mechanisms
- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
-
- Cell Image Analysis Techniques
Papers in ⓘ
-
- Face Recognition and Perception 3
- Visual perception and processing mechanisms 2
- Neural dynamics and brain function 2
-
- Visual Attention and Saliency Detection 3
- Co-authors
- Gabriel Kreiman (6 shared papers)Margaret S. Livingstone (5 shared papers)Carlos R. Ponce (3 shared papers)Till S. Hartmann (1 shared paper)Peter F. Schade (1 shared paper)Peijuan Lu (1 shared paper)Qi Long Lu (1 shared paper)Charles H. Vannoy (1 shared paper)
- Journals
- PLoS Computational Biology (2 papers)Cell (1 paper)Molecular Therapy — Methods & Clinical Development (1 paper)Proceedings of the National Academy of Sciences (1 paper)Nature Neuroscience (1 paper)
- Partner nations
- United StatesBelgiumSingapore
In The Last Decade
Will Xiao
6 papers receiving 189 citations
Peers
Comparison fields: 5 of 47
- Cognitive Neuroscience 136
- Biophysics 17
- Computer Vision and Pattern Recognition 34
- Sensory Systems 8
- Cellular and Molecular Neuroscience 24
Countries citing papers authored by Will Xiao
This map shows the geographic impact of Will Xiao'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 Will Xiao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Will Xiao more than expected).
Fields of papers citing papers by Will Xiao
This network shows the impact of papers produced by Will Xiao. 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 Will Xiao. The network helps show where Will Xiao may publish in the future.
Co-authors
The 15 scholars most cited alongside Will Xiao, 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 | 2019 | 119 | |
| 2 | 2017 | 27 | |
| 3 | 2024 | 14 | |
| 4 | 2020 | 14 | |
| 5 | 2022 | 9 | |
| 6 | 2022 | 8 | |
| 7 | Biologically-Plausible Learning Algorithms Can Scale to Large Datasets | 2018 | 0 |
| 8 | 2024 | 0 |
About Will Xiao
Will Xiao is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Molecular Biology, Sensory Systems and Biophysics, having authored 8 papers that have together received 191 indexed citations. Recurring topics across this work include Face Recognition and Perception (3 papers), Visual Attention and Saliency Detection (3 papers), Visual perception and processing mechanisms (2 papers), Cell Image Analysis Techniques (2 papers), Neural dynamics and brain function (2 papers), Olfactory and Sensory Function Studies (2 papers), Machine Learning and ELM (1 paper) and Retinal Development and Disorders (1 paper). The work is most often cited by research in Cognitive Neuroscience (136 citations), Biophysics (17 citations), Computer Vision and Pattern Recognition (34 citations), Sensory Systems (8 citations) and Cellular and Molecular Neuroscience (24 citations). Will Xiao has collaborated with scholars based in United States, Belgium and Singapore. Frequent co-authors include Gabriel Kreiman, Margaret S. Livingstone, Carlos R. Ponce, Till S. Hartmann, Peter F. Schade, Peijuan Lu, Qi Long Lu, Charles H. Vannoy, Xiao Xiao and Saloni Sharma. Their work appears in journals such as PLoS Computational Biology, Cell, Molecular Therapy — Methods & Clinical Development, Proceedings of the National Academy of Sciences and Nature Neuroscience.
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.