This map shows the geographic impact of Dan Feldman'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 Dan Feldman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Feldman more than expected).
This network shows the impact of papers produced by Dan Feldman. 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 Dan Feldman. The network helps show where Dan Feldman may publish in the future.
Co-authorship network of co-authors of Dan Feldman
This figure shows the co-authorship network connecting the top 25 collaborators of Dan Feldman.
A scholar is included among the top collaborators of Dan Feldman 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 Dan Feldman. Dan Feldman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kesseli, Aurora Y., Andrew A. West, Dan Feldman, et al.. (2020). PyHammer: Python spectral typing suite. Astrophysics Source Code Library.1 indexed citations
6.
Feldman, Dan, et al.. (2020). Sets Clustering. International Conference on Machine Learning.1 indexed citations
7.
Osadchy, Margarita, et al.. (2020). Data-Independent Neural Pruning via Coresets. International Conference on Learning Representations.3 indexed citations
8.
Feldman, Dan, et al.. (2020). Coresets for Near-Convex Functions. Neural Information Processing Systems. 33. 997–1009.2 indexed citations
9.
Braverman, Vladimir, Dan Feldman, Harry G. Lang, & Daniela Rus. (2019). Streaming coreset constructions for M-estimators. DSpace@MIT (Massachusetts Institute of Technology).4 indexed citations
10.
Zhou, Samson, et al.. (2019). On Activation Function Coresets for Network Pruning. arXiv (Cornell University).1 indexed citations
11.
Feldman, Dan, et al.. (2019). k-Means Clustering of Lines for Big Data. Neural Information Processing Systems. 32. 12797–12806.5 indexed citations
12.
Lučić, Mario, Matthew Faulkner, Andreas Krause, & Dan Feldman. (2017). Training Mixture Models at Scale via Coresets. arXiv (Cornell University).8 indexed citations
13.
Feldman, Dan, Sedat Özer, & Daniela Rus. (2017). Coresets for Vector Summarization with Applications to Network Graphs. International Conference on Machine Learning. 1117–1125.1 indexed citations
Feldman, Dan, et al.. (2015). iDiary. ACM Transactions on Sensor Networks. 11(4). 1–41.8 indexed citations
17.
Rosman, Guy, et al.. (2014). Coresets for k-Segmentation of Streaming Data. DSpace@MIT (Massachusetts Institute of Technology). 27. 559–567.24 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.