Kamalika Das
- Artificial Intelligence top 5%
- Computer Networks and Communications top 10%
- Ecology
- Media Technology top 5%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Kanishka BhaduriVed ChirayathAlan LiMichal Segal‐RozenhaimerHillol KarguptaKumar SricharanBryan MatthewsKun Liu
- Topics
- Topic Modeling (6 papers)Anomaly Detection Techniques and Applications (6 papers)Privacy-Preserving Technologies in Data (5 papers)
- Journals
- Remote Sensing of EnvironmentLecture notes in computer scienceKnowledge and Information Systems
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Kamalika Das
29 papers receiving 435 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 218
- Computer Networks and Communications 73
- Ecology 67
- Media Technology 64
- Computer Vision and Pattern Recognition 62
Countries citing papers authored by Kamalika Das
This map shows the geographic impact of Kamalika Das'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 Kamalika Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kamalika Das more than expected).
Fields of papers citing papers by Kamalika Das
This network shows the impact of papers produced by Kamalika Das. 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 Kamalika Das. The network helps show where Kamalika Das may publish in the future.
Co-authorship network of co-authors of Kamalika Das
This figure shows the co-authorship network connecting the top 25 collaborators of Kamalika Das. A scholar is included among the top collaborators of Kamalika Das 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 Kamalika Das. Kamalika Das 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 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 9 | |
| 9 | NeMO-Net – The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment | 0 |
| 10 | 118 | |
| 11 | 14 | |
| 12 | 2 | |
| 13 | 47 | |
| 14 | 13 | |
| 15 | 4 | |
| 16 | SPARSE INVERSE GAUSSIAN PROCESS REGRESSION WITH APPLICATION TO CLIMATE NETWORK DISCOVERY | 1 |
| 17 | 18 | |
| 18 | Distributed Anomaly Detection using Satellite Data From Multiple Modalitie. | 8 |
| 19 | 4 | |
| 20 | 31 |
About Kamalika Das
Kamalika Das is a scholar working on Artificial Intelligence, Signal Processing and Computer Science Applications, having authored 33 papers that have together received 454 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Anomaly Detection Techniques and Applications (6 papers) and Privacy-Preserving Technologies in Data (5 papers). The work is most often cited by research in Media Technology (64 citations), Artificial Intelligence (218 citations) and Signal Processing (47 citations). Kamalika Das has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Kanishka Bhaduri, Ved Chirayath, Alan Li, Michal Segal‐Rozenhaimer, Hillol Kargupta, Kumar Sricharan, Bryan Matthews, Kun Liu, Ashok N. Srivastava and Nikunj C. Oza. Their work appears in journals such as Remote Sensing of Environment, Lecture notes in computer science and Knowledge and Information Systems.
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.