Sumit Sanghai
Impact in
- Signal Processing top 2%
- Advanced Malware Detection Techniques
- Artificial Intelligence top 2%
- Adversarial Robustness in Machine Learning
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
Papers in
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- Bayesian Modeling and Causal Inference 4
- Topic Modeling 3
- Text and Document Classification Technologies 2
- Artificial Intelligence in Games 1
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- Advanced Database Systems and Queries 2
- Constraint Satisfaction and Optimization 2
- Co-authors
- Pedro Domingos (5 shared papers)Nilesh Dalvi (3 shared papers)Deepak Kumar Verma (1 shared paper)Mausam Mausam (1 shared paper)Qifan Wang (3 shared papers)Yang Li (3 shared papers)Prasan Roy (2 shared papers)S. Sudarshan (2 shared papers)
- Journals
- Journal of Artificial Intelligence Research (1 paper)Journal of Computer and System Sciences (1 paper)Lecture notes in computer science (1 paper)arXiv (Cornell University) (1 paper)International Joint Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Sumit Sanghai
11 papers receiving 1.0k citations
Sumit Sanghai's Hit Papers
Peers
Comparison fields: 5 of 72
- Signal Processing 305
- Artificial Intelligence 815
- Computer Networks and Communications 327
- Information Systems 247
- Management Science and Operations Research 88
Countries citing papers authored by Sumit Sanghai
This map shows the geographic impact of Sumit Sanghai'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 Sumit Sanghai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sumit Sanghai more than expected).
Fields of papers citing papers by Sumit Sanghai
This network shows the impact of papers produced by Sumit Sanghai. 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 Sumit Sanghai. The network helps show where Sumit Sanghai may publish in the future.
Co-authors
The 25 scholars most cited alongside Sumit Sanghai, 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 | Adversarial classification Hit paper breakdown → | 2004 | 614 |
| 2 | 2020 | 169 | |
| 3 | 2003 | 53 | |
| 4 | Dynamic probabilistic relational models | 2003 | 53 |
| 5 | 2020 | 52 | |
| 6 | 2001 | 51 | |
| 7 | 2005 | 38 | |
| 8 | 2022 | 32 | |
| 9 | 2012 | 14 | |
| 10 | 2005 | 7 | |
| 11 | Research on Statistical Relational Learning at the University of Washington | 2003 | 2 |
About Sumit Sanghai
Sumit Sanghai is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computational Theory and Mathematics, Information Systems and Signal Processing, having authored 11 papers that have together received 1.1k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (4 papers), Topic Modeling (3 papers), Rough Sets and Fuzzy Logic (3 papers), Advanced Database Systems and Queries (2 papers), Constraint Satisfaction and Optimization (2 papers), Data Management and Algorithms (2 papers), Text and Document Classification Technologies (2 papers) and Artificial Intelligence in Games (1 paper). The work is most often cited by research in Signal Processing (305 citations), Artificial Intelligence (815 citations), Computer Networks and Communications (327 citations), Information Systems (247 citations) and Management Science and Operations Research (88 citations). Sumit Sanghai has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Pedro Domingos, Nilesh Dalvi, Deepak Kumar Verma, Mausam Mausam, Qifan Wang, Yang Li, Prasan Roy, S. Sudarshan, Daniel S. Weld and Chris Alberti. Their work appears in journals such as Journal of Artificial Intelligence Research, Journal of Computer and System Sciences, Lecture notes in computer science, arXiv (Cornell University) and International Joint 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.