Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of David Carlson'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 Carlson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Carlson more than expected).
This network shows the impact of papers produced by David Carlson. 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 Carlson. The network helps show where David Carlson may publish in the future.
Co-authorship network of co-authors of David Carlson
This figure shows the co-authorship network connecting the top 25 collaborators of David Carlson.
A scholar is included among the top collaborators of David Carlson 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 Carlson. David Carlson is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Carlson, David. (2018). Sophomores meet the traveling salesperson problem. Journal of computing sciences in colleges. 33(3). 126–133.1 indexed citations
9.
Gallagher, Neil M., et al.. (2017). Cross-Spectral Factor Analysis. Neural Information Processing Systems. 30. 6842–6852.8 indexed citations
10.
Li, Yitong, Michael Murias, Samantha Major, et al.. (2017). Targeting EEG/LFP Synchrony with Neural Nets. Neural Information Processing Systems. 30. 4621–4631.30 indexed citations
11.
Carlson, David, et al.. (2016). Partition functions from Rao-Blackwellized tempered sampling. DukeSpace (Duke University). 6. 2896–2905.3 indexed citations
12.
Carlson, David, Volkan Cevher, & Lawrence Carin. (2015). Stochastic Spectral Descent for Restricted Boltzmann Machines. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 111–119.17 indexed citations
13.
Gan, Zhe, Changyou Chen, Ricardo Henao, David Carlson, & Lawrence Carin. (2015). Scalable Deep Poisson Factor Analysis for Topic Modeling. International Conference on Machine Learning. 1823–1832.32 indexed citations
14.
Chen, Changyou, David Carlson, Zhe Gan, Chunyuan Li, & Lawrence Carin. (2015). Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. International Conference on Artificial Intelligence and Statistics. 1051–1060.6 indexed citations
15.
Gan, Zhe, Ricardo Henao, David Carlson, & Lawrence Carin. (2015). Learning Deep Sigmoid Belief Networks with Data Augmentation. International Conference on Artificial Intelligence and Statistics. 268–276.49 indexed citations
16.
Carlson, David, et al.. (2013). Designed Measurements for Vector Count Data. Neural Information Processing Systems. 26. 1142–1150.11 indexed citations
17.
Carlson, David. (2006). The Status and Outlook for the Photovoltaics Industry. Bulletin of the American Physical Society.2 indexed citations
18.
Joannopoulos, J. D., G. Lucovsky, & David Carlson. (1984). Electronic and vibrational properties. Springer eBooks.1 indexed citations
Carlson, David, et al.. (1976). Solar cells using Schottky barriers on amorphous silicon. Photovoltaic Specialists Conference. 893.3 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.