Soham Sarkar
- Statistics and Probability top 5%
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Biomaterials
- Polymers and Plastics
- Co-authors
- Anil K. GhoshSusanta LahiriBasudam AdhikariMunmun BiswasB.B. ChaudhuriSamarendra MajiSanjoy SadhukhanSwapan K. Parui
- Topics
- Statistical Methods and Inference (11 papers)Bayesian Methods and Mixture Models (5 papers)Advanced Statistical Methods and Models (4 papers)
- Partner nations
- IndiaSwitzerlandUnited States
In The Last Decade
Soham Sarkar
16 papers receiving 141 citations
Peers
Comparison fields: 5 of 73
- Statistics and Probability 52
- Artificial Intelligence 51
- Computer Vision and Pattern Recognition 27
- Biomaterials 19
- Polymers and Plastics 11
Countries citing papers authored by Soham Sarkar
This map shows the geographic impact of Soham Sarkar'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 Soham Sarkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soham Sarkar more than expected).
Fields of papers citing papers by Soham Sarkar
This network shows the impact of papers produced by Soham Sarkar. 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 Soham Sarkar. The network helps show where Soham Sarkar may publish in the future.
Co-authorship network of co-authors of Soham Sarkar
This figure shows the co-authorship network connecting the top 25 collaborators of Soham Sarkar. A scholar is included among the top collaborators of Soham Sarkar 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 Soham Sarkar. Soham Sarkar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 39 | |
| 9 | 7 | |
| 10 | 8 | |
| 11 | 4 | |
| 12 | 10 | |
| 13 | 11 | |
| 14 | Bioremediation of heavy metals from Neem (Azadirachta indica) leaf extract by chelation with dithizone (A prospective and effective method for pharmaceutical industry) | 2 |
| 15 | 10 | |
| 16 | 12 | |
| 17 | 17 | |
| 18 | 10 |
About Soham Sarkar
Soham Sarkar is a scholar working on Statistics and Probability, Computer Graphics and Computer-Aided Design and Numerical Analysis, having authored 18 papers that have together received 145 indexed citations. Recurring topics across this work include Statistical Methods and Inference (11 papers), Bayesian Methods and Mixture Models (5 papers) and Advanced Statistical Methods and Models (4 papers). The work is most often cited by research in Statistics and Probability (52 citations), Computational Mathematics (3 citations) and Artificial Intelligence (51 citations). Soham Sarkar has collaborated with scholars based in India, Switzerland and United States. Frequent co-authors include Anil K. Ghosh, Susanta Lahiri, Basudam Adhikari, Munmun Biswas, B.B. Chaudhuri, Samarendra Maji, Sanjoy Sadhukhan, Swapan K. Parui, Sanchareeka Dey and Prosanta Singha. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Technometrics and Biometrika.
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.