David Feng

18.8k total citations
29 papers, 729 citations indexed

About

David Feng is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Signal Processing. According to data from OpenAlex, David Feng has authored 29 papers receiving a total of 729 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 10 papers in Cognitive Neuroscience and 7 papers in Signal Processing. Recurrent topics in David Feng's work include Neural dynamics and brain function (9 papers), Computer Graphics and Visualization Techniques (5 papers) and Data Visualization and Analytics (5 papers). David Feng is often cited by papers focused on Neural dynamics and brain function (9 papers), Computer Graphics and Visualization Techniques (5 papers) and Data Visualization and Analytics (5 papers). David Feng collaborates with scholars based in United States, Australia and Canada. David Feng's co-authors include Hongkui Zeng, Michael Hawrylycz, Christof Koch, Lydia Ng, Susan M. Sunkin, Yang Li, Amy Bernard, Nathan W. Gouwens, Russell M. Taylor and Jim Berg and has published in prestigious journals such as Nature Communications, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

David Feng

27 papers receiving 712 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Feng United States 12 405 240 123 115 100 29 729
Vincent J. Dercksen Germany 11 327 0.8× 294 1.2× 78 0.6× 41 0.4× 50 0.5× 15 526
Felix Schürmann Switzerland 21 792 2.0× 571 2.4× 250 2.0× 65 0.6× 268 2.7× 56 1.5k
Edward G. Jones United States 14 542 1.3× 435 1.8× 166 1.3× 100 0.9× 22 0.2× 21 1.2k
Yoonsuck Choe United States 14 468 1.2× 143 0.6× 84 0.7× 122 1.1× 224 2.2× 108 924
Leif H. Finkel United States 21 1.2k 2.9× 741 3.1× 219 1.8× 250 2.2× 55 0.6× 61 1.7k
Grace W. Lindsay United States 7 505 1.2× 159 0.7× 51 0.4× 135 1.2× 69 0.7× 11 838
Sergey Yurgenson United States 5 650 1.6× 483 2.0× 183 1.5× 30 0.3× 75 0.8× 7 924
Payam A. Saisan United States 9 128 0.3× 205 0.9× 44 0.4× 203 1.8× 65 0.7× 15 729
J. I. Nelson United States 20 1.5k 3.7× 606 2.5× 247 2.0× 105 0.9× 55 0.6× 33 1.7k
Erhan Bas United States 14 286 0.7× 289 1.2× 217 1.8× 211 1.8× 33 0.3× 32 971

Countries citing papers authored by David Feng

Since Specialization
Citations

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

Fields of papers citing papers by David Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Feng

This figure shows the co-authorship network connecting the top 25 collaborators of David Feng. A scholar is included among the top collaborators of David Feng 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 David Feng. David Feng 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.
2.
Buccino, Alessio Paolo, Olivier Winter, David Bryant, et al.. (2023). Compression strategies for large-scale electrophysiology data. Journal of Neural Engineering. 20(5). 56009–56009. 3 indexed citations
3.
Tappan, Susan, Brian S. Eastwood, Nathan O’Connor, et al.. (2019). Automatic navigation system for the mouse brain. The Journal of Comparative Neurology. 527(13). 2200–2211. 20 indexed citations
4.
Waters, Jack, Eric Lee, Nathalie Gaudreault, et al.. (2019). Biological variation in the sizes, shapes and locations of visual cortical areas in the mouse. PLoS ONE. 14(5). e0213924–e0213924. 13 indexed citations
5.
Teeter, Corinne, Ramakrishnan Iyer, Vilas Menon, et al.. (2018). Generalized leaky integrate-and-fire models classify multiple neuron types. Nature Communications. 9(1). 709–709. 129 indexed citations
6.
Gratiy, Sergey L., Yazan N. Billeh, Kael Dai, et al.. (2018). BioNet: A Python interface to NEURON for modeling large-scale networks. PLoS ONE. 13(8). e0201630–e0201630. 34 indexed citations
7.
Gouwens, Nathan W., Jim Berg, David Feng, et al.. (2018). Systematic generation of biophysically detailed models for diverse cortical neuron types. Nature Communications. 9(1). 710–710. 78 indexed citations
8.
Feng, David, Chris Lau, Lydia Ng, et al.. (2015). Exploration and visualization of connectivity in the adult mouse brain. Methods. 73. 90–97. 11 indexed citations
9.
Kuan, Leonard, Yang Li, Chris Lau, et al.. (2014). Neuroinformatics of the Allen Mouse Brain Connectivity Atlas. Methods. 73. 4–17. 131 indexed citations
10.
Vied, Cynthia, Florian Freudenberg, Yuting Wang, et al.. (2014). A multi-resource data integration approach: identification of candidate genes regulating cell proliferation during neocortical development. Frontiers in Neuroscience. 8. 257–257. 16 indexed citations
11.
Ng, Lydia, Susan M. Sunkin, David Feng, et al.. (2012). Large-Scale Neuroinformatics for In Situ Hybridization Data in the Mouse Brain. International review of neurobiology. 104. 159–182. 5 indexed citations
12.
Feng, David, Lester Kwock, Yueh Z. Lee, & Russell M. Taylor. (2010). Matching Visual Saliency to Confidence in Plots of Uncertain Data. IEEE Transactions on Visualization and Computer Graphics. 16(6). 980–989. 48 indexed citations
13.
Feng, David, Lester Kwock, Yueh Z. Lee, & Russell M. Taylor. (2009). Linked exploratory visualizations for uncertain MR spectroscopy data. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7530. 753004–753004. 9 indexed citations
14.
Feng, David, et al.. (2008). Automatic construction of 3D models from architectural line drawings. 6. 123–130. 15 indexed citations
15.
Feng, David, et al.. (2007). Stepping into the Third Dimension. Journal of Neuroscience. 27(47). 12757–12760. 33 indexed citations
16.
Feng, David, et al.. (2006). Perception-based construction of 3D models from line drawings. 53–53. 5 indexed citations
17.
Feng, David, et al.. (2005). Fast adaptive variable frame-rate coding. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 5. 2734–2738. 4 indexed citations
18.
Cai, Wentong, et al.. (2005). Interactive multi-volume visualization of segmented structures within PET-CT data sets. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 1 indexed citations
19.
Feng, David, et al.. (2004). A mixed scheme to improve subjective quality in low bitrate video. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 1754–1759. 1 indexed citations
20.
Feng, David & Matthew A. Mauro. (2003). SIR 2003 Film Panel Case 6: Hemosuccus Pancreaticus Secondary to Chronic Pancreatitis. Journal of Vascular and Interventional Radiology. 14(6). 803–805. 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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