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.
Machine Learning Methods for Attack Detection in the Smart Grid
2015441 citationsSanjeev R. Kulkarni, H. Vincent Poor 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 Sanjeev R. Kulkarni
Since
Specialization
Citations
This map shows the geographic impact of Sanjeev R. Kulkarni'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 Sanjeev R. Kulkarni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanjeev R. Kulkarni more than expected).
Fields of papers citing papers by Sanjeev R. Kulkarni
This network shows the impact of papers produced by Sanjeev R. Kulkarni. 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 Sanjeev R. Kulkarni. The network helps show where Sanjeev R. Kulkarni may publish in the future.
Co-authorship network of co-authors of Sanjeev R. Kulkarni
This figure shows the co-authorship network connecting the top 25 collaborators of Sanjeev R. Kulkarni.
A scholar is included among the top collaborators of Sanjeev R. Kulkarni 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 Sanjeev R. Kulkarni. Sanjeev R. Kulkarni is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Amiri, Mohammad Mohammadi, Tolga M. Duman, Denız Gündüz, Sanjeev R. Kulkarni, & H. Vincent Poor. (2021). Blind Federated Edge Learning. Iris Unimore (University of Modena and Reggio Emilia).70 indexed citations
7.
Amiri, Mohammad Mohammadi, Denız Gündüz, Sanjeev R. Kulkarni, & H. Vincent Poor. (2020). Update Aware Device Scheduling for Federated Learning at the Wireless Edge. arXiv (Cornell University).4 indexed citations
8.
Gao, Peng, Xusheng Xiao, Li Ding, et al.. (2018). SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection. USENIX Security Symposium. 639–656.4 indexed citations
9.
Gao, Peng, Xusheng Xiao, Zhichun Li, et al.. (2018). AIQL: enabling efficient attack investigation from system monitoring data. arXiv (Cornell University). 113–125.15 indexed citations
Wang, Tiance, et al.. (2014). The application of differential privacy for rank aggregation: Privacy and accuracy. arXiv (Cornell University). 1–7.1 indexed citations
12.
Zhang, Zhuo & Sanjeev R. Kulkarni. (2014). Detection of shilling attacks in recommender systems via spectral clustering. International Conference on Information Fusion. 1–8.31 indexed citations
13.
Busch, Michael W., Sanjeev R. Kulkarni, S. J. Ostro, et al.. (2009). Delay-Doppler and Radar-Interferometric Imaging of the Near-Earth Asteroid 2008 EV5. DPS.1 indexed citations
George, Abraham, Warren B. Powell, & Sanjeev R. Kulkarni. (2008). Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management. Journal of Machine Learning Research. 9(68). 2079–2111.32 indexed citations
Lozano, Aurélie, Sanjeev R. Kulkarni, & Robert E. Schapire. (2005). Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. Neural Information Processing Systems. 18. 819–826.22 indexed citations
18.
Harman, Gilbert & Sanjeev R. Kulkarni. (2003). Inductive Simplicity and the Matrix. eScholarship (California Digital Library). 25(25).1 indexed citations
19.
Kulkarni, Sanjeev R., Gábor Lugosi, & Santosh S. Venkatesh. (2000). Learning pattern classification— a survey (invited paper). IEEE Press eBooks. 134–162.1 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.