Kashif Rasul
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Text Readability and Simplification
- Text and Document Classification Technologies
Papers in
-
- Natural Language Processing Techniques 1
- Authorship Attribution and Profiling 1
- Gaussian Processes and Bayesian Inference 1
- Topic Modeling 1
- Anomaly Detection Techniques and Applications 1
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- Time Series Analysis and Forecasting 1
- Co-authors
- Alan Akbik (1 shared paper)Stefan Schweter (1 shared paper)Duncan A. J. Blythe (1 shared paper)Roland Vollgraf (2 shared papers)Urs Bergmann (1 shared paper)Ingmar Schuster (1 shared paper)
- Journals
- Communications in Analysis and Geometry (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- GermanyUnited States
In The Last Decade
Kashif Rasul
3 papers receiving 166 citations
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 158
- Computational Mathematics 2
- Issues, ethics and legal aspects 2
- Information Systems 27
- Management Science and Operations Research 12
Countries citing papers authored by Kashif Rasul
This map shows the geographic impact of Kashif Rasul'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 Kashif Rasul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kashif Rasul more than expected).
Fields of papers citing papers by Kashif Rasul
This network shows the impact of papers produced by Kashif Rasul. 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 Kashif Rasul. The network helps show where Kashif Rasul may publish in the future.
Co-authors
The 6 scholars most cited alongside Kashif Rasul, 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 | 2019 | 179 | |
| 2 | Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows | 2021 | 2 |
| 3 | 2010 | 2 |
About Kashif Rasul
Kashif Rasul is a scholar working on Artificial Intelligence, Signal Processing, Applied Mathematics, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 183 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (1 paper), Geometric Analysis and Curvature Flows (1 paper), Time Series Analysis and Forecasting (1 paper), Authorship Attribution and Profiling (1 paper), Point processes and geometric inequalities (1 paper), Gaussian Processes and Bayesian Inference (1 paper), Topic Modeling (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (158 citations), Computational Mathematics (2 citations), Issues, ethics and legal aspects (2 citations), Information Systems (27 citations) and Management Science and Operations Research (12 citations). Kashif Rasul has collaborated with scholars based in Germany and United States. Frequent co-authors include Alan Akbik, Stefan Schweter, Duncan A. J. Blythe, Roland Vollgraf, Urs Bergmann and Ingmar Schuster. Their work appears in journals such as Communications in Analysis and Geometry and arXiv (Cornell University).
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.