Amol Deshpande

8.8k total citations · 3 hit papers
106 papers, 4.6k citations indexed

About

Amol Deshpande is a scholar working on Computer Networks and Communications, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Amol Deshpande has authored 106 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Computer Networks and Communications, 51 papers in Signal Processing and 47 papers in Artificial Intelligence. Recurrent topics in Amol Deshpande's work include Data Management and Algorithms (50 papers), Advanced Database Systems and Queries (48 papers) and Graph Theory and Algorithms (19 papers). Amol Deshpande is often cited by papers focused on Data Management and Algorithms (50 papers), Advanced Database Systems and Queries (48 papers) and Graph Theory and Algorithms (19 papers). Amol Deshpande collaborates with scholars based in United States, United Kingdom and Canada. Amol Deshpande's co-authors include Samuel Madden, Joseph M. Hellerstein, Wei Hong, Vijayshankar Raman, Prithviraj Sen, Michael J. Franklin, Sirish Chandrasekaran, Mehul A. Shah, Owen Cooper and Sailesh Krishnamurthy and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, SAE technical papers on CD-ROM/SAE technical paper series and Pattern Recognition Letters.

In The Last Decade

Amol Deshpande

101 papers receiving 4.3k citations

Hit Papers

align trajectories

What are hit papers?

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.

TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.

2003 649 citations Sirish Chandrasekaran, Owen Cooper et al. Conference on Innovative Data Systems Research profile →
TelegraphCQ

2003 480 citations Sirish Chandrasekaran, Owen Cooper et al. profile →
Approximate Data Collection in Sensor Networks using Probabilistic Models

2006 330 citations Amol Deshpande, Joseph M. Hellerstein et al. profile →

Peers

Amol Deshpande

CNC

SP

AI

IS

CVPR

Eugene J. Shekita United States

Stan Zdonik United States

Nesime Tatbul United States

Shivnath Babu United States

Minos Garofalakis United States

Patrick Valduriez France

Stanley B. Zdonik United States

Martin Kersten Netherlands

Alfons Kemper Germany

Justin Ma United States

Eugene J. Shekita United States

Amol Deshpande

3.4k ×1.0

CNC

2.0k ×0.9

SP

2.2k ×1.2

AI

1.5k ×1.3

IS

637 ×0.8

CVPR

Citations per year, relative to Amol Deshpande Amol Deshpande (= 1×) peers Eugene J. Shekita

Countries citing papers authored by Amol Deshpande

Since Specialization

Citations

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

Fields of papers citing papers by Amol Deshpande

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amol Deshpande

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Deshpande, Amol, et al.. (2025). Comprehending C codes with LLMs: Effective comment generation through retrieval and reasoning. Pattern Recognition Letters. 199. 295–302. 1 indexed citations

2.

Khuller, Samir, et al.. (2024). To Store or Not to Store: a graph theoretical approach for Dataset Versioning. 479–493.

3.

Deshpande, Amol, et al.. (2022). Early Stage Detection of Multiple Sclerosis using FCNN. 1–4. 1 indexed citations

4.

Deshpande, Amol, et al.. (2020). Bluetooth Based Electronic Notice Board.. International Journal of Engineering and Advanced Technology. 10(1). 178–181.

5.

Deshpande, Amol, et al.. (2018). ProvDB: Provenance-enabled Lifecycle Management of Collaborative Data Analysis Workflows.. IEEE Data(base) Engineering Bulletin. 41. 26–38. 6 indexed citations

6.

Yan, Da, Yingyi Bu, Yuanyuan Tian, & Amol Deshpande. (2017). Big Graph Analytics Platforms. 7(1-2). 1–195. 48 indexed citations

7.

Deshpande, Amol, et al.. (2016). Approximation Algorithms for Stochastic Submodular Set Cover with Applications to Boolean Function Evaluation and Min-Knapsack. ACM Transactions on Algorithms. 12(3). 1–28. 14 indexed citations

8.

Rekatsinas, Theodoros, Amol Deshpande, Dong Xin, Lise Getoor, & Divesh Srivastava. (2016). SourceSight. 2157–2160. 9 indexed citations

9.

Chavan, Amit, et al.. (2014). DataHub: Collaborative Data Science & Dataset Version Management at Scale. arXiv (Cornell University). 77 indexed citations

10.

Deshpande, Amol, et al.. (2014). Approximation algorithms for stochastic boolean function evaluation and stochastic submodular set cover. Symposium on Discrete Algorithms. 1453–1467. 20 indexed citations

11.

Khurana, Udayan & Amol Deshpande. (2013). Efficient snapshot retrieval over historical graph data. 997–1008. 109 indexed citations

12.

Li, Jian & Amol Deshpande. (2010). Ranking continuous probabilistic datasets. Proceedings of the VLDB Endowment. 3(1-2). 638–649. 27 indexed citations

13.

Deshpande, Amol, Zachary G. Ives, & Vijayshankar Raman. (2007). Adaptive query processing: why, how, when, what next?. Very Large Data Bases. 1426–1427. 8 indexed citations

14.

Deshpande, Amol & Sunita Sarawagi. (2007). Probabilistic graphical models and their role in databases. Very Large Data Bases. 1435–1436. 8 indexed citations

15.

Deshpande, Amol, Zachary G. Ives, & Vijayshankar Raman. (2007). Adaptive Query Processing. 1(1). 1–140. 119 indexed citations

16.

Deshpande, Amol, Carlos Guestrin, & Samuel Madden. (2005). Using Probabilistic Models for Data Management in Acquisitional Environments. Conference on Innovative Data Systems Research. 317–328. 61 indexed citations

17.

Deshpande, Amol, Carlos Guestrin, & Samuel Madden. (2005). Resource-Aware Wireless Sensor-Actuator Networks.. IEEE Data(base) Engineering Bulletin. 28(3). 40–47. 33 indexed citations

18.

Deshpande, Amol, Carlos Guestrin, Wan Pyo Hong, & Samuel Madden. (2005). Exploiting Correlated Attributes in Acquisitional Query Processing. 143–154. 84 indexed citations

19.

Chandrasekaran, Sirish, Owen Cooper, Amol Deshpande, et al.. (2003). TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.. Conference on Innovative Data Systems Research. 649 indexed citations breakdown →

20.

Hellerstein, Joseph M., Michael J. Franklin, Sirish Chandrasekaran, et al.. (2000). Adaptive Query Processing: Technology in Evolution.. IEEE Data(base) Engineering Bulletin. 23. 7–18. 142 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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