Paras Sheth
Impact in
- Artificial Intelligence top 10%
- Bayesian Modeling and Causal Inference
- Explainable Artificial Intelligence (XAI)
- Hate Speech and Cyberbullying Detection
- Advanced Graph Neural Networks
- Machine Learning and Data Classification
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- Advanced Causal Inference Techniques
Papers in
-
- Bayesian Modeling and Causal Inference 5
- Hate Speech and Cyberbullying Detection 3
- Adversarial Robustness in Machine Learning 2
- Machine Learning and Data Classification 2
- Co-authors
- Huan Liu (15 shared papers)Raha Moraffah (5 shared papers)K. Selçuk Candan (9 shared papers)Lu Cheng (3 shared papers)Qianru Wang (1 shared paper)Adrienne Raglin (1 shared paper)Ruocheng Guo (3 shared papers)Mansooreh Karami (1 shared paper)
- Journals
- ACM Transactions on Spatial Algorithms and Systems (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)Knowledge and Information Systems (1 paper)IEEE Transactions on Artificial Intelligence (1 paper)2022 IEEE International Conference on Big Data (Big Data) (2 papers)
- Partner nations
- United StatesHong KongRussia
In The Last Decade
Paras Sheth
15 papers receiving 185 citations
Peers
Comparison fields: 5 of 77
- Artificial Intelligence 101
- Statistics and Probability 18
- Signal Processing 17
- Geography, Planning and Development 5
- Modeling and Simulation 4
Countries citing papers authored by Paras Sheth
This map shows the geographic impact of Paras Sheth'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 Paras Sheth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paras Sheth more than expected).
Fields of papers citing papers by Paras Sheth
This network shows the impact of papers produced by Paras Sheth. 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 Paras Sheth. The network helps show where Paras Sheth may publish in the future.
Co-authors
The 19 scholars most cited alongside Paras Sheth, 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 | 2021 | 72 | |
| 2 | 2022 | 42 | |
| 3 | 2024 | 14 | |
| 4 | 2024 | 10 | |
| 5 | 2023 | 9 | |
| 6 | 2024 | 8 | |
| 7 | 2021 | 7 | |
| 8 | 2022 | 5 | |
| 9 | 2022 | 5 | |
| 10 | 2023 | 4 | |
| 11 | 2023 | 4 | |
| 12 | 2023 | 4 | |
| 13 | 2021 | 2 | |
| 14 | 2024 | 1 | |
| 15 | 2024 | 1 | |
| 16 | 2026 | 0 | |
| 17 | 2025 | 0 | |
| 18 | 2024 | 0 |
About Paras Sheth
Paras Sheth is a scholar working on Artificial Intelligence, Sociology and Political Science, Information Systems, Statistical and Nonlinear Physics and Environmental Engineering, having authored 18 papers that have together received 188 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (5 papers), Hate Speech and Cyberbullying Detection (3 papers), Adversarial Robustness in Machine Learning (2 papers), Advanced Causal Inference Techniques (2 papers), Hydrological Forecasting Using AI (2 papers), Statistical Methods and Inference (2 papers), Machine Learning and Data Classification (2 papers) and Hydrology and Watershed Management Studies (2 papers). The work is most often cited by research in Artificial Intelligence (101 citations), Statistics and Probability (18 citations), Signal Processing (17 citations), Geography, Planning and Development (5 citations) and Modeling and Simulation (4 citations). Paras Sheth has collaborated with scholars based in United States, Hong Kong and Russia. Frequent co-authors include Huan Liu, Raha Moraffah, K. Selçuk Candan, Lu Cheng, Qianru Wang, Adrienne Raglin, Ruocheng Guo, Mansooreh Karami, Aman Chadha and John L. Sabo. Their work appears in journals such as ACM Transactions on Spatial Algorithms and Systems, ACM Transactions on Knowledge Discovery from Data, Knowledge and Information Systems, IEEE Transactions on Artificial Intelligence and 2022 IEEE International Conference on Big Data (Big Data).
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.