Kaustav Das
Impact in
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications
- Imbalanced Data Classification Techniques
- Internet Traffic Analysis and Secure E-voting
- Data Stream Mining Techniques
- Signal Processing top 10%
- Time Series Analysis and Forecasting
Papers in
-
- Anomaly Detection Techniques and Applications 5
- Imbalanced Data Classification Techniques 1
-
- Network Security and Intrusion Detection 3
- Co-authors
- Jeff Schneider (4 shared papers)Daniel B. Neill (1 shared paper)Yixiao Wang (1 shared paper)Andrew Moore (1 shared paper)Mark R. Baker (1 shared paper)S. Sarkar (1 shared paper)Santanu Banerjee (1 shared paper)Stuart N. Baker (1 shared paper)
- Journals
- Neuromodulation Technology at the Neural Interface (1 paper)Public Choice (1 paper)Sedimentary Geology (1 paper)Paladyn Journal of Behavioral Robotics (1 paper)
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Kaustav Das
8 papers receiving 183 citations
Peers
Comparison fields: 5 of 39
- Artificial Intelligence 176
- Signal Processing 44
- Computer Networks and Communications 88
- Statistics and Probability 23
- Computational Mathematics 1
Countries citing papers authored by Kaustav Das
This map shows the geographic impact of Kaustav 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 Kaustav Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaustav Das more than expected).
Fields of papers citing papers by Kaustav Das
This network shows the impact of papers produced by Kaustav 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 Kaustav Das. The network helps show where Kaustav Das may publish in the future.
Co-authors
The 12 scholars most cited alongside Kaustav Das, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 118 | |
| 2 | 2008 | 63 | |
| 3 | Detecting patterns of anomalies | 2009 | 7 |
| 4 | 2004 | 4 | |
| 5 | 2021 | 3 | |
| 6 | 2022 | 1 | |
| 7 | 2024 | 1 | |
| 8 | 2004 | 1 | |
| 9 | 2024 | 0 | |
| 10 | 2025 | 0 |
About Kaustav Das
Kaustav Das is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Epidemiology and Political Science and International Relations, having authored 10 papers that have together received 198 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (5 papers), Network Security and Intrusion Detection (3 papers), Data-Driven Disease Surveillance (2 papers), Time Series Analysis and Forecasting (2 papers), Imbalanced Data Classification Techniques (1 paper), Fiscal Policies and Political Economy (1 paper), Geological and Geophysical Studies (1 paper) and Electoral Systems and Political Participation (1 paper). The work is most often cited by research in Artificial Intelligence (176 citations), Signal Processing (44 citations), Computer Networks and Communications (88 citations), Statistics and Probability (23 citations) and Computational Mathematics (1 citation). Kaustav Das has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Jeff Schneider, Daniel B. Neill, Yixiao Wang, Andrew Moore, Mark R. Baker, S. Sarkar, Santanu Banerjee, Stuart N. Baker, Koel Mukherjee and Pushkar Maitra. Their work appears in journals such as Neuromodulation Technology at the Neural Interface, Public Choice, Sedimentary Geology and Paladyn Journal of Behavioral Robotics.
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.