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.
Cross-layer design for wireless networks
2003545 citationsSanjay Shakkottai et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Sanjay Shakkottai
Since
Specialization
Citations
This map shows the geographic impact of Sanjay Shakkottai'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 Sanjay Shakkottai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanjay Shakkottai more than expected).
Fields of papers citing papers by Sanjay Shakkottai
This network shows the impact of papers produced by Sanjay Shakkottai. 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 Sanjay Shakkottai. The network helps show where Sanjay Shakkottai may publish in the future.
Co-authorship network of co-authors of Sanjay Shakkottai
This figure shows the co-authorship network connecting the top 25 collaborators of Sanjay Shakkottai.
A scholar is included among the top collaborators of Sanjay Shakkottai 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 Sanjay Shakkottai. Sanjay Shakkottai is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Caramanis, Constantine, et al.. (2021). Contextual Blocking Bandits. International Conference on Artificial Intelligence and Statistics. 271–279.1 indexed citations
4.
Sen, Rajat, Kirthevasan Kandasamy, & Sanjay Shakkottai. (2019). Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach. International Conference on Artificial Intelligence and Statistics. 2096–2105.3 indexed citations
5.
Sen, Rajat, et al.. (2019). Blocking Bandits. Neural Information Processing Systems. 32. 4784–4793.1 indexed citations
6.
Dullerud, Geir E., et al.. (2019). Optimistic Optimization for Statistical Model Checking with Regret Bounds.. arXiv (Cornell University).1 indexed citations
Ray, Avik, Joe Neeman, Sujay Sanghavi, & Sanjay Shakkottai. (2018). The Search Problem in Mixture Models. Journal of Machine Learning Research. 18(206). 1–61.4 indexed citations
Sen, Rajat, Karthikeyan Shanmugam, & Sanjay Shakkottai. (2018). Contextual Bandits with Stochastic Experts.. arXiv (Cornell University). 852–861.1 indexed citations
11.
Sen, Rajat, Kirthevasan Kandasamy, & Sanjay Shakkottai. (2018). Multi-Fidelity Black-Box Optimization with Hierarchical Partitions. International Conference on Machine Learning. 4538–4547.12 indexed citations
12.
Sen, Rajat, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, & Sanjay Shakkottai. (2017). Model-Powered Conditional Independence Test. Neural Information Processing Systems. 30. 2951–2961.3 indexed citations
13.
Sanghavi, Sujay, Sanjay Shakkottai, Marc Lelarge, & Bianca Schroeder. (2014). The 2014 ACM international conference on Measurement and modeling of computer systems.5 indexed citations
14.
Nasipuri, Asis, Yih‐Chun Hu, & Sanjay Shakkottai. (2012). Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing.1 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.