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.
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
200334.8k citationsPaul Shannon, Owen Ozier et al.Genome Researchprofile →
Communication-Efficient Learning of Deep Networks from Decentralized Data
20162.6k citationsH. Brendan McMahan, Eider Moore et al.arXiv (Cornell University)profile →
Practical Secure Aggregation for Privacy-Preserving Machine Learning
20171.9k citationsKeith Bonawitz, H. Brendan McMahan et al.profile →
Labeled LDA
2009848 citationsDaniel Ramage, Christopher D. Manning et al.profile →
Characterizing Microblogs with Topic Models
2010489 citationsDaniel Ramage, Susan Dumais et al.Proceedings of the International AAAI Conference on Web and Social Mediaprofile →
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning
2022133 citationsPeter Kairouz, Daniel Ramage et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Daniel Ramage'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 Daniel Ramage with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Ramage more than expected).
This network shows the impact of papers produced by Daniel Ramage. 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 Daniel Ramage. The network helps show where Daniel Ramage may publish in the future.
Co-authorship network of co-authors of Daniel Ramage
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Ramage.
A scholar is included among the top collaborators of Daniel Ramage 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 Daniel Ramage. Daniel Ramage is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bonawitz, Kallista, Peter Kairouz, Brendan McMahan, & Daniel Ramage. (2021). Federated Learning and Privacy. Queue. 19(5). 87–114.43 indexed citations
4.
Acharya, Jayadev, Keith Bonawitz, Peter Kairouz, Daniel Ramage, & Ziteng Sun. (2020). Context Aware Local Differential Privacy. International Conference on Machine Learning. 1. 52–62.1 indexed citations
5.
McMahan, H. Brendan, Daniel Ramage, Kunal Talwar, & Li Zhang. (2017). Learning Differentially Private Language Models Without Losing Accuracy.39 indexed citations
6.
Kairouz, Peter, Keith Bonawitz, & Daniel Ramage. (2016). Discrete distribution estimation under local privacy. International Conference on Machine Learning. 2436–2444.81 indexed citations
7.
McMahan, H. Brendan, et al.. (2016). Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv (Cornell University). 1273–1282.2590 indexed citations breakdown →
Chuang, Jason, Daniel Ramage, Christopher D. Manning, & Jeffrey Heer. (2012). Interpretation and trust. 443–452.172 indexed citations
10.
Ramage, Daniel, et al.. (2011). A Study of Academic Collaborations in Computational Linguistics using a Latent Mixture of Authors Model. Meeting of the Association for Computational Linguistics. 124–132.4 indexed citations
11.
Ramage, Daniel, et al.. (2011). A study of academic collaboration in computational linguistics with latent mixtures of authors. Meeting of the Association for Computational Linguistics. 124–132.7 indexed citations
Teevan, Jaime, et al.. (2011). #TwitterSearch. 35–44.241 indexed citations
14.
Ramage, Daniel, Susan Dumais, & Dan Liebling. (2010). Characterizing Microblogs with Topic Models. Proceedings of the International AAAI Conference on Web and Social Media. 4(1). 130–137.489 indexed citations breakdown →
Ramage, Daniel, Paul Heymann, Christopher D. Manning, & Héctor García-Molina. (2009). Clustering the tagged web. 54–63.141 indexed citations
17.
Marneffe, Marie-Catherine de, Trond Grenager, Bill MacCartney, et al.. (2007). Robust Graph Alignment Methods for Textual Inference and Machine Reading.. National Conference on Artificial Intelligence. 36–42.2 indexed citations
18.
Hughes, T. A. & Daniel Ramage. (2007). Lexical Semantic Relatedness with Random Graph Walks. Empirical Methods in Natural Language Processing. 581–589.125 indexed citations
19.
Ramage, Daniel & Adam J. Oliner. (2007). RA. 19–19.2 indexed citations
20.
Shannon, Paul, Owen Ozier, Nitin S. Baliga, et al.. (2003). Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research. 13(11). 2498–2504.34768 indexed citations breakdown →
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.