Madhur Srivastava
- Molecular Biology
- Biophysics top 2%
- Computer Vision and Pattern Recognition top 10%
- Materials Chemistry
- Physiology
- Co-authors
- Jack H. FreedC. Lindsay AndersonElka R. GeorgievaYann FichouJennifer N. RauchAbhinav GuptaSongi HanYanxian Lin
- Topics
- Electron Spin Resonance Studies (10 papers)NMR spectroscopy and applications (6 papers)Image and Signal Denoising Methods (5 papers)
- Partner nations
- United StatesIndiaNetherlands
In The Last Decade
Madhur Srivastava
26 papers receiving 552 citations
Peers
Comparison fields: 5 of 107
- Molecular Biology 150
- Biophysics 125
- Computer Vision and Pattern Recognition 121
- Materials Chemistry 71
- Physiology 67
Countries citing papers authored by Madhur Srivastava
This map shows the geographic impact of Madhur Srivastava'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 Madhur Srivastava with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Madhur Srivastava more than expected).
Fields of papers citing papers by Madhur Srivastava
This network shows the impact of papers produced by Madhur Srivastava. 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 Madhur Srivastava. The network helps show where Madhur Srivastava may publish in the future.
Co-authorship network of co-authors of Madhur Srivastava
This figure shows the co-authorship network connecting the top 25 collaborators of Madhur Srivastava. A scholar is included among the top collaborators of Madhur Srivastava 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 Madhur Srivastava. Madhur Srivastava is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 7 | |
| 9 | 0 | |
| 10 | 9 | |
| 11 | 11 | |
| 12 | 20 | |
| 13 | 86 | |
| 14 | 44 | |
| 15 | 198 | |
| 16 | Regression and ARIMA hybrid model for new bug prediction | 0 |
| 17 | 4 | |
| 18 | Image Processing Methods for the Restoration of Digitized Paintings | 15 |
| 19 | 14 | |
| 20 | 5 |
About Madhur Srivastava
Madhur Srivastava is a scholar working on Biophysics, Nuclear and High Energy Physics and Computer Vision and Pattern Recognition, having authored 30 papers that have together received 564 indexed citations. Recurring topics across this work include Electron Spin Resonance Studies (10 papers), NMR spectroscopy and applications (6 papers) and Image and Signal Denoising Methods (5 papers). The work is most often cited by research in Biophysics (125 citations), Computer Vision and Pattern Recognition (121 citations) and Media Technology (48 citations). Madhur Srivastava has collaborated with scholars based in United States, India and Netherlands. Frequent co-authors include Jack H. Freed, C. Lindsay Anderson, Elka R. Georgieva, Yann Fichou, Jennifer N. Rauch, Abhinav Gupta, Songi Han, Yanxian Lin, Timothy J. Keller and Kenneth S. Kosik. Their work appears in journals such as Proceedings of the National Academy of Sciences, ACS Nano and The Journal of Physical Chemistry B.
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.