Ashwin Pananjady

25 papers receiving 251 citations

Peers

Ashwin Pananjady
Comparison fields: 5 of 48
  • Statistics and Probability 60
  • Computational Mathematics 3
  • Artificial Intelligence 102
  • Computer Networks and Communications 70
  • Applied Mathematics 31
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Citations per year

Countries citing papers authored by Ashwin Pananjady

Since Specialization
Citations

This map shows the geographic impact of Ashwin Pananjady'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 Ashwin Pananjady with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ashwin Pananjady more than expected).

Fields of papers citing papers by Ashwin Pananjady

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ashwin Pananjady. 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 Ashwin Pananjady. The network helps show where Ashwin Pananjady may publish in the future.

Co-authors

The 19 scholars most cited alongside Ashwin Pananjady, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ashwin Pananjady Line = papers co-authored together Ashwin Pananjady links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201754
2 201850
3 201929
4 201629
5 201814
6 202011
7 202210
8 20239
9 20207
10 20187
11 20155
12 20215
13 20215
14 20205
15
Optimally Approximating the Lifetime of Wireless Sensor Networks.
20134
16 20173
17 20242
18 20202
19 20222
20
Breaking the $1/\sqrt{n}$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time
20181

About Ashwin Pananjady

Ashwin Pananjady is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computational Mechanics, Statistics and Probability and Management Science and Operations Research, having authored 28 papers that have together received 259 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (7 papers), Distributed Sensor Networks and Detection Algorithms (5 papers), Stochastic Gradient Optimization Techniques (4 papers), Machine Learning and Algorithms (3 papers), Statistical Methods and Inference (3 papers), Advanced Bandit Algorithms Research (3 papers), Consumer Market Behavior and Pricing (2 papers) and Cellular Automata and Applications (2 papers). The work is most often cited by research in Statistics and Probability (60 citations), Computational Mathematics (3 citations), Artificial Intelligence (102 citations), Computer Networks and Communications (70 citations) and Applied Mathematics (31 citations). Ashwin Pananjady has collaborated with scholars based in United States, France and India. Frequent co-authors include Thomas A. Courtade, Martin J. Wainwright, Max Fathi, Vivek Bagaria, Rahul Vaze, Peter L. Bartlett, Kush Bhatia, Richard J. Samworth, Dean P. Foster and Christos Thrampoulidis. Their work appears in journals such as IEEE Transactions on Information Theory, The Annals of Statistics, Mathematics of Operations Research, Information and Inference A Journal of the IMA and IEEE/ACM Transactions on Networking.

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.

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