Prudhvi Gurram

1.1k total citations
48 papers, 671 citations indexed

About

Prudhvi Gurram is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Prudhvi Gurram has authored 48 papers receiving a total of 671 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Media Technology, 17 papers in Computer Vision and Pattern Recognition and 15 papers in Biomedical Engineering. Recurrent topics in Prudhvi Gurram's work include Remote-Sensing Image Classification (21 papers), Advanced Chemical Sensor Technologies (10 papers) and Remote Sensing and Land Use (9 papers). Prudhvi Gurram is often cited by papers focused on Remote-Sensing Image Classification (21 papers), Advanced Chemical Sensor Technologies (10 papers) and Remote Sensing and Land Use (9 papers). Prudhvi Gurram collaborates with scholars based in United States, United Kingdom and Australia. Prudhvi Gurram's co-authors include Heesung Kwon, Raghuveer Rao, Simon Julier, Richard Tomsett, Mani Srivastava, Supriyo Chakraborty, Shuowen Hu, Dave Braines, Alun Preece and Federico Cerutti and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, Optics Letters and IEEE Transactions on Aerospace and Electronic Systems.

In The Last Decade

Prudhvi Gurram

47 papers receiving 641 citations

Peers

Prudhvi Gurram

CVPR

Jiangbin Zheng China

Shijin Li China

Xiaoyan Luo China

Youqiang Zhang China

Ning Ma China

Zengmao Wang China

Pablo Ribalta Lorenzo Poland

Shangbing Gao China

Fang He China

Jiangbin Zheng China

Prudhvi Gurram

172 ×0.7

249 ×1.4

234 ×1.4

CVPR

138 ×1.4

39 ×0.5

Citations per year, relative to Prudhvi Gurram Prudhvi Gurram (= 1×) peers Jiangbin Zheng

Countries citing papers authored by Prudhvi Gurram

Since Specialization

Citations

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

Fields of papers citing papers by Prudhvi Gurram

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prudhvi Gurram

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

All Works

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20 of 20 papers shown

Gurram, Prudhvi, et al.. (2023). Minimax asymptotically optimal quickest change detection for statistically periodic data. Signal Processing. 215. 109290–109290. 1 indexed citations

Bai, He, et al.. (2020). Decentralized Langevin Dynamics for Bayesian Learning. Neural Information Processing Systems. 33. 15978–15989. 4 indexed citations

Gurram, Prudhvi, et al.. (2020). Multislot and Multistream Quickest Change Detection in Statistically Periodic Processes. 1147–1152. 1 indexed citations

Chakraborty, Supriyo, Richard Tomsett, Ramya Raghavendra, et al.. (2017). Interpretability of deep learning models: A survey of results. ECS Journal of Solid State Science and Technology (The Electrochemical Society). 1–6. 246 indexed citations

Gurram, Prudhvi, Heesung Kwon, & Charles E. Davidson. (2016). Coalition Game Theory-Based Feature Subspace Selection for Hyperspectral Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(6). 2354–2364. 7 indexed citations

Hu, Shuowen, Nathaniel J. Short, Benjamin S. Riggan, et al.. (2016). A Polarimetric Thermal Database for Face Recognition Research. 187–194. 50 indexed citations

Peng, Zhimin, Prudhvi Gurram, Heesung Kwon, & Wotao Yin. (2015). Sparse kernel learning-based feature selection for anomaly detection. IEEE Transactions on Aerospace and Electronic Systems. 51(3). 1698–1716. 15 indexed citations

Gurram, Prudhvi, Heesung Kwon, & Charles E. Davidson. (2015). Shapely value based random subspace selection for hyperspectral image classification. 4975–4978. 1 indexed citations

Rao, Raghuveer & Prudhvi Gurram. (2014). Entropy formulations for signal reconstruction from sensor arrays. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9103. 91030I–91030I.

10.

Hu, Shuowen, et al.. (2014). Thermal-to-visible face recognition using multiple kernel learning. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9091. 909110–909110. 2 indexed citations

11.

Gurram, Prudhvi, Shuowen Hu, & Anthony K.C. Chan. (2013). Uniform grid upsampling of 3D lidar point cloud data. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8650. 86500B–86500B. 10 indexed citations

12.

Kwon, Heesung, Xiaofei Hu, James Theiler, Alina Zare, & Prudhvi Gurram. (2013). Algorithms for Multispectral and Hyperspectral Image Analysis. Journal of Electrical and Computer Engineering. 2013. 1–2. 6 indexed citations

13.

Gurram, Prudhvi & Heesung Kwon. (2012). Kernel-based joint spectral and spatial exploitation using Hilbert space embedding for hyperspectral classification. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8390. 83901R–83901R. 2 indexed citations

14.

Gurram, Prudhvi & Heesung Kwon. (2012). Sparse Kernel-Based Ensemble Learning With Fully Optimized Kernel Parameters for Hyperspectral Classification Problems. IEEE Transactions on Geoscience and Remote Sensing. 51(2). 787–802. 24 indexed citations

15.

Gurram, Prudhvi & Heesung Kwon. (2012). Optimal sparse kernel learning in the Empirical Kernel Feature Space for hyperspectral classification. 7. 1–4. 1 indexed citations

16.

Gurram, Prudhvi, et al.. (2011). Hyperspectral anomaly detection using sparse kernel-based ensemble learning. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8048. 80481C–80481C. 2 indexed citations

17.

Gurram, Prudhvi & Heesung Kwon. (2011). Support-Vector-Based Hyperspectral Anomaly Detection Using Optimized Kernel Parameters. IEEE Geoscience and Remote Sensing Letters. 8(6). 1060–1064. 28 indexed citations

18.

Kwon, Heesung & Prudhvi Gurram. (2010). Optimal kernel bandwidth estimation for hyperspectral kernel-based anomaly detection. 2812–2815. 8 indexed citations

19.

Gurram, Prudhvi, et al.. (2009). A Segment-Based Mesh Design for Building Parallel-Perspective Stereo Mosaics. IEEE Transactions on Geoscience and Remote Sensing. 48(3). 1256–1269. 4 indexed citations

20.

Gurram, Prudhvi, Sohail A. Dianat, Lalit K. Mestha, & Raja Bala. (2005). Comparison of 1-D, 2-D and 3-D Printer Calibration Algorithms with Printer Drift. Technical programs and proceedings. 21(1). 505–510. 2 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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