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.
Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks
2014904 citationsPaulo Tabuada, Suhas Diggavi et al.IEEE Transactions on Automatic Controlprofile →
Wireless Network Information Flow: A Deterministic Approach
2011499 citationsSuhas Diggavi, David Tse et al.IEEE Transactions on Information Theoryprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Suhas Diggavi'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 Suhas Diggavi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suhas Diggavi more than expected).
This network shows the impact of papers produced by Suhas Diggavi. 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 Suhas Diggavi. The network helps show where Suhas Diggavi may publish in the future.
Co-authorship network of co-authors of Suhas Diggavi
This figure shows the co-authorship network connecting the top 25 collaborators of Suhas Diggavi.
A scholar is included among the top collaborators of Suhas Diggavi 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 Suhas Diggavi. Suhas Diggavi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Girgis, Antonious M., Deepesh Data, Suhas Diggavi, Peter Kairouz, & Ananda Theertha Suresh. (2021). Shuffled Model of Differential Privacy in Federated Learning. International Conference on Artificial Intelligence and Statistics. 2521–2529.20 indexed citations
7.
Fragouli, Christina, et al.. (2021). Group testing for connected communities. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 130. 2341–2349.14 indexed citations
Karakus, Can, Yifan Sun, Suhas Diggavi, & Wotao Yin. (2017). Straggler Mitigation in Distributed Optimization Through Data Encoding. Neural Information Processing Systems. 30. 5434–5442.39 indexed citations
11.
Diggavi, Suhas, et al.. (2016). Password Cracking: The Effect of Bias on the Average Guesswork of Hash Functions.. arXiv (Cornell University).1 indexed citations
12.
Karamchandani, Nikhil, et al.. (2014). Coded Caching for Heterogeneous Wireless Networks with Multi-level Access.. arXiv (Cornell University).11 indexed citations
13.
Prabhakaran, Vinod M., et al.. (2012). On interactive message secrecy over erasure networks. Infoscience (Ecole Polytechnique Fédérale de Lausanne).1 indexed citations
14.
Tian, Chao & Suhas Diggavi. (2006). On scalable source coding for multiple decoders with side-information. Infoscience (Ecole Polytechnique Fédérale de Lausanne).4 indexed citations
15.
Fragouli, Christina, et al.. (2006). Topology inferene using network coding. Infoscience (Ecole Polytechnique Fédérale de Lausanne).1 indexed citations
16.
Diggavi, Suhas & David Tse. (2004). On successive refinement of diversity. Infoscience (Ecole Polytechnique Fédérale de Lausanne).8 indexed citations
17.
Al‐Dhahir, Naofal & Suhas Diggavi. (2002). On achievable rates on time-varying frequency-selective channels. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 860–865.1 indexed citations
18.
Diggavi, Suhas, Matthias Grossglauser, & David Tse. (2002). Even one-dimensional mobility increases capacity of wireless adhoc networks. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 352.11 indexed citations
19.
Fragouli, Christina, Naofal Al‐Dhahir, Suhas Diggavi, & William Turin. (2001). Prefiltered Space-Time M-BCJR Equalizer for Frequency-Selective Channels. Infoscience (Ecole Polytechnique Fédérale de Lausanne).2 indexed citations
20.
Raleigh, G.G., Suhas Diggavi, V.K. Jones, & A. Paulraj. (1995). Blind Adaptive Transmit Beamforming For Mobile Radio With Arbitrary and Unknown Antenna Array Geometries. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1494–1499.3 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.