Brian Cheung

3.8k total citations · 1 hit paper
42 papers, 1.7k citations indexed

About

Brian Cheung is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Brian Cheung has authored 42 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 8 papers in Aerospace Engineering. Recurrent topics in Brian Cheung's work include Target Tracking and Data Fusion in Sensor Networks (15 papers), Robotics and Sensor-Based Localization (5 papers) and Adversarial Robustness in Machine Learning (4 papers). Brian Cheung is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (15 papers), Robotics and Sensor-Based Localization (5 papers) and Adversarial Robustness in Machine Learning (4 papers). Brian Cheung collaborates with scholars based in Australia, United States and Canada. Brian Cheung's co-authors include R. Cameron Craddock, F. Xavier Castellanos, Michael P. Milham, Qingyang Li, Adriana Di Martino, Stan Colcombe, Chao‐Gan Yan, Clare Kelly, Xi‐Nian Zuo and Samuel J. Davey and has published in prestigious journals such as Nature Communications, NeuroImage and Sensors.

In The Last Decade

Brian Cheung

41 papers receiving 1.7k citations

Hit Papers

A comprehensive assessment of regional variation in the i... 2013 2026 2017 2021 2013 400 800 1.2k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Brian Cheung Australia 11 1.1k 478 353 237 210 42 1.7k
Ling Zou China 18 598 0.5× 262 0.5× 173 0.5× 104 0.4× 81 0.4× 135 1.4k
Aapo Nummenmaa United States 29 991 0.9× 1.0k 2.2× 475 1.3× 84 0.4× 218 1.0× 88 2.7k
Pan Lin China 30 1.3k 1.2× 387 0.8× 110 0.3× 328 1.4× 82 0.4× 114 2.7k
Simon G. Fabri Malta 14 993 0.9× 191 0.4× 254 0.7× 83 0.4× 53 0.3× 78 1.9k
Visar Berisha United States 24 329 0.3× 90 0.2× 588 1.7× 298 1.3× 128 0.6× 155 2.1k
Iead Rezek United Kingdom 18 1.0k 0.9× 188 0.4× 356 1.0× 170 0.7× 25 0.1× 36 2.0k
Guangyu Zhou United States 28 784 0.7× 204 0.4× 120 0.3× 124 0.5× 38 0.2× 66 2.2k
Jie Xiang China 25 1.8k 1.6× 403 0.8× 274 0.8× 343 1.4× 19 0.1× 140 2.6k
Luiz Antonio Baccalá Brazil 20 2.8k 2.5× 229 0.5× 266 0.8× 142 0.6× 31 0.1× 63 3.5k
Wenqin Wang China 19 716 0.6× 299 0.6× 95 0.3× 240 1.0× 89 0.4× 58 1.7k

Countries citing papers authored by Brian Cheung

Since Specialization
Citations

This map shows the geographic impact of Brian Cheung'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 Cheung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Cheung more than expected).

Fields of papers citing papers by Brian Cheung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian Cheung. 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 Cheung. The network helps show where Brian Cheung may publish in the future.

Co-authorship network of co-authors of Brian Cheung

This figure shows the co-authorship network connecting the top 25 collaborators of Brian Cheung. A scholar is included among the top collaborators of Brian Cheung 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 Cheung. Brian Cheung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yardley, Heath, et al.. (2025). Space Surveillance with High-Frequency Radar. Sensors. 25(7). 2302–2302. 1 indexed citations
2.
Moazzami, Kasra, Brian Cheung, Samaah Sullivan, et al.. (2023). Hemodynamic Reactivity to Mental Stress in Patients With Coronary Artery Disease. JAMA Network Open. 6(10). e2338060–e2338060. 6 indexed citations
3.
Shankar, Shreya, Brian Cheung, Nicolas Papernot, et al.. (2023). Subtle adversarial image manipulations influence both human and machine perception. Nature Communications. 14(1). 4933–4933. 3 indexed citations
5.
Cheung, Brian, et al.. (2022). Ocular Impression-Based Scleral Lens With Wavefront-Guided Optics for Visual Improvement in Keratoconus. Eye & Contact Lens Science & Clinical Practice. 48(11). 485–488. 3 indexed citations
6.
Cheung, Brian, Alexander V. Terekhov, Yubei Chen, Pulkit Agrawal, & Bruno A. Olshausen. (2019). Superposition of many models into one. arXiv (Cornell University). 32. 10867–10876. 16 indexed citations
7.
Metz, Luke, Niru Maheswaranathan, Brian Cheung, & Jascha Sohl‐Dickstein. (2018). Learning Unsupervised Learning Rules. arXiv (Cornell University). 9 indexed citations
8.
Metz, Luke, Niru Maheswaranathan, Brian Cheung, & Jascha Sohl‐Dickstein. (2018). Meta-Learning Update Rules for Unsupervised Representation Learning. International Conference on Learning Representations. 4 indexed citations
9.
Cheung, Brian, Mark Rutten, Samuel J. Davey, & Gregory Cohen. (2018). Probabilistic Multi Hypothesis Tracker for an Event Based Sensor. 1–8. 14 indexed citations
10.
García-García, Manuel, Aki Nikolaidis, Pierre Bellec, et al.. (2017). Detecting stable individual differences in the functional organization of the human basal ganglia. NeuroImage. 170. 68–82. 12 indexed citations
11.
Cheung, Brian, et al.. (2016). Emergence of foveal image sampling from learning to attend in visual scenes. arXiv (Cornell University). 1 indexed citations
12.
Lin, Ching‐Yi, et al.. (2015). Differential intensity-dependent effects of magnetic stimulation on the longest neurites and shorter dendrites in neuroscreen-1 cells. Journal of Neural Engineering. 12(2). 26013–26013. 4 indexed citations
13.
Davey, Samuel J. & Brian Cheung. (2015). A Linear Complexity Approximate Method for Multi-Target Particle Filter Track before Detect. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 6. 1–6. 1 indexed citations
14.
Cheung, Brian, Jesse A. Livezey, Arjun K. Bansal, & Bruno A. Olshausen. (2014). Discovering Hidden Factors of Variation in Deep Networks. arXiv (Cornell University). 8 indexed citations
15.
Yan, Chao‐Gan, Brian Cheung, Clare Kelly, et al.. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage. 76. 183–201. 1208 indexed citations breakdown →
16.
Cheung, Brian, et al.. (2009). Hardware-Optimized Ziggurat Algorithm for High-Speed Gaussian Random Number Generators.. 254–260. 9 indexed citations
17.
Davey, Samuel J., Brian Cheung, & Mark Rutten. (2009). Track-Before-Detect for sensors with complex measurements. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 618–625. 7 indexed citations
18.
Davey, Samuel J., et al.. (2006). Separation of Target Rigid Body and Micro-Doppler Effects in ISAR Imaging. 1 indexed citations
19.
Davey, Samuel J., et al.. (2006). Clutter Rejection using Peak Curvature. IEEE Transactions on Aerospace and Electronic Systems. 42(4). 1492–1496. 6 indexed citations
20.
Davey, Samuel J., et al.. (2004). PDAF versus PMHT performance on OTHR data. i. 560–565. 4 indexed citations

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.

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