Sidharth Mudgal
Impact in
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- Data Quality and Management
- Artificial Intelligence top 5%
- Topic Modeling
- Privacy-Preserving Technologies in Data
- Natural Language Processing Techniques
- Semantic Web and Ontologies
- Advanced Graph Neural Networks
Papers in
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- Topic Modeling 5
- Semantic Web and Ontologies 3
- Natural Language Processing Techniques 2
- Speech Recognition and Synthesis 1
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- Data Quality and Management 5
- Co-authors
- AnHai Doan (5 shared papers)Theodoros Rekatsinas (2 shared papers)Youngchoon Park (2 shared papers)Han Li (1 shared paper)Esteban Arcaute (1 shared paper)Ganesh Krishnan (1 shared paper)Yingyu Liang (1 shared paper)Haojun Zhang (3 shared papers)
- Journals
- Journal of Computer Science and Cybernetics (1 paper)IEEE Data(base) Engineering Bulletin (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- United States
In The Last Decade
Sidharth Mudgal
7 papers receiving 322 citations
Sidharth Mudgal's Hit Papers
Peers
Comparison fields: 5 of 38
- Management Science and Operations Research 255
- Artificial Intelligence 295
- Information Systems 98
- Information Systems and Management 16
- Signal Processing 23
Countries citing papers authored by Sidharth Mudgal
This map shows the geographic impact of Sidharth Mudgal'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 Sidharth Mudgal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sidharth Mudgal more than expected).
Fields of papers citing papers by Sidharth Mudgal
This network shows the impact of papers produced by Sidharth Mudgal. 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 Sidharth Mudgal. The network helps show where Sidharth Mudgal may publish in the future.
Co-authors
The 24 scholars most cited alongside Sidharth Mudgal, 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 | Deep Learning for Entity Matching Hit paper breakdown → | 2018 | 282 |
| 2 | 2018 | 23 | |
| 3 | 2017 | 20 | |
| 4 | 2019 | 12 | |
| 5 | 2021 | 10 | |
| 6 | Toward a System Building Agenda for Data Integration (and Data Science). | 2018 | 3 |
| 7 | 2021 | 1 |
About Sidharth Mudgal
Sidharth Mudgal is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Information Systems and Management and Signal Processing, having authored 7 papers that have together received 351 indexed citations. Recurring topics across this work include Data Quality and Management (5 papers), Topic Modeling (5 papers), Semantic Web and Ontologies (3 papers), Natural Language Processing Techniques (2 papers), Advanced Database Systems and Queries (2 papers), Data Management and Algorithms (1 paper), Scientific Computing and Data Management (1 paper) and Speech Recognition and Synthesis (1 paper). The work is most often cited by research in Management Science and Operations Research (255 citations), Artificial Intelligence (295 citations), Information Systems (98 citations), Information Systems and Management (16 citations) and Signal Processing (23 citations). Sidharth Mudgal has collaborated with scholars based in United States. Frequent co-authors include AnHai Doan, Theodoros Rekatsinas, Youngchoon Park, Han Li, Esteban Arcaute, Ganesh Krishnan, Yingyu Liang, Haojun Zhang, Pradap Konda and Sanjib Das. Their work appears in journals such as Journal of Computer Science and Cybernetics, IEEE Data(base) Engineering Bulletin and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.