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.
Comparing top k lists
2003487 citationsRonald Fagin, Ravi Kumar et al.Symposium on Discrete Algorithmsprofile →
Evolutionary clustering
2006424 citationsRavi Kumar, Andrew Tomkins et al.profile →
Scalable k-means++
2012405 citationsBenjamin Moseley, Andrea Vattani 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 Ravi Kumar'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 Ravi Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ravi Kumar more than expected).
This network shows the impact of papers produced by Ravi Kumar. 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 Ravi Kumar. The network helps show where Ravi Kumar may publish in the future.
Co-authorship network of co-authors of Ravi Kumar
This figure shows the co-authorship network connecting the top 25 collaborators of Ravi Kumar.
A scholar is included among the top collaborators of Ravi Kumar 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 Ravi Kumar. Ravi Kumar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kumar, Ravi, Rina Panigrahy, Ali Rahimi, & David P. Woodruff. (2019). Faster Algorithms for Binary Matrix Factorization. International Conference on Machine Learning. 3551–3559.5 indexed citations
11.
Kumar, Ravi, et al.. (2016). Wither Marriage? Divorce Mania in Bangalore City: A Critical Analysis. International Journal of Advanced Research in Management and Social Sciences. 5(2). 17–26.3 indexed citations
12.
Gupta, Rishi, Ravi Kumar, & Sergei Vassilvitskii. (2016). On Mixtures of Markov Chains. Neural Information Processing Systems. 29. 3441–3449.6 indexed citations
13.
Kumar, Ravi, et al.. (2016). Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces. International Conference on Artificial Intelligence and Statistics. 948–956.6 indexed citations
14.
Dasgupta, Anirban, Ravi Kumar, & Sujith Ravi. (2013). Summarization Through Submodularity and Dispersion. Meeting of the Association for Computational Linguistics. 1014–1022.24 indexed citations
15.
Kumar, Ravi, Daniel Lokshtanov, Sergei Vassilvitskii, & Andrea Vattani. (2013). Near-Optimal Bounds for Cross-Validation via Loss Stability. International Conference on Machine Learning. 27–35.4 indexed citations
16.
Das, Abhimanyu, Anirban Dasgupta, & Ravi Kumar. (2012). Selecting Diverse Features via Spectral Regularization. Neural Information Processing Systems. 25. 1583–1591.20 indexed citations
17.
Fagin, Ronald, et al.. (2005). Efficient implementation of large-scale multi-structural databases. Very Large Data Bases. 958–969.11 indexed citations
18.
Fagin, Ronald, Ravi Kumar, & D. Sivakumar. (2003). Comparing top k lists. Symposium on Discrete Algorithms. 17(1). 28–36.487 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.