Shashank Mujumdar
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Management Science and Operations Research
- Information Systems
- Computer Networks and Communications
- Co-authors
- Nitin GuptaSameep MehtaRuhi Sharma MittalShanmukha GuttulaShazia AfzalHima PatelVitobha MunigalaAbhinav Jain
- Topics
- Data Quality and Management (3 papers)Anomaly Detection Techniques and Applications (3 papers)Advanced Image and Video Retrieval Techniques (2 papers)
- Journals
- Proceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- IndiaUnited StatesIsrael
In The Last Decade
Shashank Mujumdar
10 papers receiving 238 citations
Peers
Comparison fields: 5 of 92
- Artificial Intelligence 108
- Computer Vision and Pattern Recognition 36
- Management Science and Operations Research 34
- Information Systems 33
- Computer Networks and Communications 21
Countries citing papers authored by Shashank Mujumdar
This map shows the geographic impact of Shashank Mujumdar'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 Shashank Mujumdar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shashank Mujumdar more than expected).
Fields of papers citing papers by Shashank Mujumdar
This network shows the impact of papers produced by Shashank Mujumdar. 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 Shashank Mujumdar. The network helps show where Shashank Mujumdar may publish in the future.
Co-authorship network of co-authors of Shashank Mujumdar
This figure shows the co-authorship network connecting the top 25 collaborators of Shashank Mujumdar. A scholar is included among the top collaborators of Shashank Mujumdar 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 Shashank Mujumdar. Shashank Mujumdar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | 59 | |
| 4 | 156 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 5 | |
| 8 | 3 | |
| 9 | 3 | |
| 10 | A Novel Framework for Segmentation of Stroke Lesions in Diffusion Weighted MRI Using Multiple b-Value Data | 7 |
| 11 | 1 |
About Shashank Mujumdar
Shashank Mujumdar is a scholar working on Computer Vision and Pattern Recognition, Management Science and Operations Research and Health Information Management, having authored 11 papers that have together received 245 indexed citations. Recurring topics across this work include Data Quality and Management (3 papers), Anomaly Detection Techniques and Applications (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Health Informatics (6 citations), Artificial Intelligence (108 citations) and Management Science and Operations Research (34 citations). Shashank Mujumdar has collaborated with scholars based in India, United States and Israel. Frequent co-authors include Nitin Gupta, Sameep Mehta, Ruhi Sharma Mittal, Shanmukha Guttula, Shazia Afzal, Hima Patel, Vitobha Munigala, Abhinav Jain, Sambaran Bandyopadhyay and Suranjana Samanta. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence.
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.