Ashwin Pananjady

607 total citations
28 papers, 256 citations indexed

About

Ashwin Pananjady is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computational Mechanics. According to data from OpenAlex, Ashwin Pananjady has authored 28 papers receiving a total of 256 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 9 papers in Computer Networks and Communications and 7 papers in Computational Mechanics. Recurrent topics in Ashwin Pananjady's work include Sparse and Compressive Sensing Techniques (7 papers), Distributed Sensor Networks and Detection Algorithms (5 papers) and Stochastic Gradient Optimization Techniques (4 papers). Ashwin Pananjady is often cited by papers focused on Sparse and Compressive Sensing Techniques (7 papers), Distributed Sensor Networks and Detection Algorithms (5 papers) and Stochastic Gradient Optimization Techniques (4 papers). Ashwin Pananjady collaborates with scholars based in United States, India and Canada. Ashwin Pananjady's co-authors include Thomas A. Courtade, Martin J. Wainwright, Max Fathi, Rahul Vaze, Vivek Bagaria, Peter L. Bartlett, Kush Bhatia, Richard J. Samworth, Dean P. Foster and Christos Thrampoulidis and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and The Annals of Statistics.

In The Last Decade

Ashwin Pananjady

25 papers receiving 248 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ashwin Pananjady United States 8 99 69 58 51 47 28 256
Daniel Dadush Netherlands 10 92 0.9× 35 0.5× 38 0.7× 24 0.5× 160 3.4× 37 282
César A. Uribe United States 10 126 1.3× 123 1.8× 10 0.2× 41 0.8× 20 0.4× 59 287
Stanislav Minsker United States 8 111 1.1× 24 0.3× 139 2.4× 11 0.2× 22 0.5× 21 276
Adityanand Guntuboyina United States 10 105 1.1× 24 0.3× 216 3.7× 12 0.2× 19 0.4× 27 332
Alex Dytso United States 10 94 0.9× 127 1.8× 55 0.9× 186 3.6× 16 0.3× 79 369
Yuyuan Ouyang United States 5 141 1.4× 27 0.4× 20 0.3× 19 0.4× 104 2.2× 17 348
Yiqiao Zhong United States 7 79 0.8× 18 0.3× 98 1.7× 11 0.2× 12 0.3× 14 295
Rasmus Kyng United States 8 79 0.8× 55 0.8× 39 0.7× 13 0.3× 122 2.6× 17 217
Jorma K. Merikoski Finland 10 40 0.4× 19 0.3× 18 0.3× 75 1.5× 160 3.4× 62 321
Russell Bradford United Kingdom 11 40 0.4× 117 1.7× 8 0.1× 22 0.4× 88 1.9× 37 287

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-authorship network of co-authors of Ashwin Pananjady

This figure shows the co-authorship network connecting the top 25 collaborators of Ashwin Pananjady. A scholar is included among the top collaborators of Ashwin Pananjady 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 Ashwin Pananjady. Ashwin Pananjady is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Pananjady, Ashwin, et al.. (2024). Do Algorithms and Barriers for Sparse Principal Component Analysis Extend to Other Structured Settings?. IEEE Transactions on Signal Processing. 72. 3187–3200. 2 indexed citations
3.
Mou, Wenlong, Ashwin Pananjady, Martin J. Wainwright, & Peter L. Bartlett. (2024). Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. 7(1). 41–153. 1 indexed citations
4.
Pananjady, Ashwin, et al.. (2024). Alternating minimization for generalized rank-1 matrix sensing: sharp predictions from a random initialization. Information and Inference A Journal of the IMA. 13(3).
5.
Lan, Guanghui, et al.. (2023). Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation. SIAM Journal on Mathematics of Data Science. 5(1). 174–200. 1 indexed citations
6.
Pananjady, Ashwin, et al.. (2023). Sharp global convergence guarantees for iterative nonconvex optimization with random data. The Annals of Statistics. 51(1). 9 indexed citations
7.
Zhang, Sheng, Ashwin Pananjady, & Justin Romberg. (2022). A Dual Accelerated Method for a Class of Distributed Optimization Problems: From Consensus to Decentralized Policy Evaluation. 2022 IEEE 61st Conference on Decision and Control (CDC). 5220–5225. 2 indexed citations
8.
Pananjady, Ashwin & Richard J. Samworth. (2022). Isotonic regression with unknown permutations: Statistics, computation and adaptation. The Annals of Statistics. 50(1). 9 indexed citations
9.
Ghosh, Avishek, Ashwin Pananjady, Adityanand Guntuboyina, & Kannan Ramchandran. (2021). Max-Affine Regression: Parameter Estimation for Gaussian Designs. IEEE Transactions on Information Theory. 68(3). 1851–1885. 4 indexed citations
10.
Ghosh, Avishek, Ashwin Pananjady, Adityanand Guntuboyina, & Kannan Ramchandran. (2020). Max-affine regression with universal parameter estimation for small-ball designs. 2706–2710. 2 indexed citations
11.
Pananjady, Ashwin & Martin J. Wainwright. (2020). Instance-Dependent ℓ∞-Bounds for Policy Evaluation in Tabular Reinforcement Learning. IEEE Transactions on Information Theory. 67(1). 566–585. 11 indexed citations
12.
Courtade, Thomas A., Max Fathi, & Ashwin Pananjady. (2019). Existence of Stein kernels under a spectral gap, and discrepancy bounds. Annales de l Institut Henri Poincaré Probabilités et Statistiques. 55(2). 29 indexed citations
13.
Pananjady, Ashwin, et al.. (2018). Breaking the $1/\sqrt{n}$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time. arXiv (Cornell University). 2037–2042. 1 indexed citations
14.
Pananjady, Ashwin & Thomas A. Courtade. (2018). The Effect of Local Decodability Constraints on Variable-Length Compression. IEEE Transactions on Information Theory. 64(4). 2593–2608. 7 indexed citations
15.
Pananjady, Ashwin, et al.. (2018). Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. arXiv (Cornell University). 21(21). 1–2925. 50 indexed citations
16.
Courtade, Thomas A., Max Fathi, & Ashwin Pananjady. (2017). Wasserstein stability of the entropy power inequality for log-concave random vectors. 119. 659–663. 3 indexed citations
17.
Pananjady, Ashwin, Martin J. Wainwright, & Thomas A. Courtade. (2017). Linear Regression With Shuffled Data: Statistical and Computational Limits of Permutation Recovery. IEEE Transactions on Information Theory. 64(5). 3286–3300. 54 indexed citations
18.
Pananjady, Ashwin, et al.. (2016). On the Complexity of Making a Distinguished Vertex Minimum or Maximum Degree by Vertex Deletion. 1 indexed citations
19.
Pananjady, Ashwin & Thomas A. Courtade. (2015). Compressing sparse sequences under local decodability constraints. 2979–2983. 5 indexed citations
20.
Bagaria, Vivek, Ashwin Pananjady, & Rahul Vaze. (2013). Optimally Approximating the Lifetime of Wireless Sensor Networks.. arXiv (Cornell University). 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.

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