Sunil Mohan
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Machine Learning in Healthcare
- Semantic Web and Ontologies
Papers in
-
- Topic Modeling 7
- Natural Language Processing Techniques 3
- Advanced Text Analysis Techniques 2
- Machine Learning and Algorithms 2
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- Biomedical Text Mining and Ontologies 5
- Co-authors
- Zhiyong Lu (3 shared papers)Nicolas Fiorini (3 shared papers)Donghui Li (1 shared paper)Sun Kim (2 shared papers)Weng‐Keen Wong (1 shared paper)Jun Yu (1 shared paper)Kathi Canese (1 shared paper)Maxim Osipov (1 shared paper)
- Journals
- PLoS Biology (1 paper)Machine Learning (1 paper)arXiv (Cornell University) (3 papers)NASA Technical Reports Server (NASA) (1 paper)
- Partner nations
- United States
In The Last Decade
Sunil Mohan
9 papers receiving 171 citations
Peers
Comparison fields: 5 of 64
- Health Informatics 9
- Artificial Intelligence 120
- Statistics, Probability and Uncertainty 13
- Information Systems 35
- Research and Theory 1
Countries citing papers authored by Sunil Mohan
This map shows the geographic impact of Sunil Mohan'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 Sunil Mohan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sunil Mohan more than expected).
Fields of papers citing papers by Sunil Mohan
This network shows the impact of papers produced by Sunil Mohan. 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 Sunil Mohan. The network helps show where Sunil Mohan may publish in the future.
Co-authors
The 16 scholars most cited alongside Sunil Mohan, 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 | 2018 | 76 | |
| 2 | 2014 | 30 | |
| 3 | 2018 | 23 | |
| 4 | 2019 | 20 | |
| 5 | 2017 | 16 | |
| 6 | 2020 | 8 | |
| 7 | Automating Mission Scheduling for Space-Based Observatories | 1998 | 6 |
| 8 | 2021 | 3 | |
| 9 | Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition. | 2019 | 1 |
About Sunil Mohan
Sunil Mohan is a scholar working on Artificial Intelligence, Molecular Biology, Information Systems, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 183 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Biomedical Text Mining and Ontologies (5 papers), Natural Language Processing Techniques (3 papers), Advanced Text Analysis Techniques (2 papers), Machine Learning and Algorithms (2 papers), Information Retrieval and Search Behavior (2 papers), Advanced Image and Video Retrieval Techniques (1 paper) and Distributed systems and fault tolerance (1 paper). The work is most often cited by research in Health Informatics (9 citations), Artificial Intelligence (120 citations), Statistics, Probability and Uncertainty (13 citations), Information Systems (35 citations) and Research and Theory (1 citation). Sunil Mohan has collaborated with scholars based in United States. Frequent co-authors include Zhiyong Lu, Nicolas Fiorini, Donghui Li, Sun Kim, Weng‐Keen Wong, Jun Yu, Kathi Canese, Maxim Osipov, James Ostell and Vadim Miller. Their work appears in journals such as PLoS Biology, Machine Learning, arXiv (Cornell University) and NASA Technical Reports Server (NASA).
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.