Chirag Agarwal
Impact in
- Health Informatics top 10%
-
- Long-Term Effects of COVID-19
Papers in
-
- Cardiovascular Function and Risk Factors 3
- Cardiac Valve Diseases and Treatments 3
- Cardiovascular Disease and Adiposity 2
- Co-authors
- Marinka ŽitnikHimabindu LakkarajuDan SchonfeldAnh‐Tu NguyenMohammad R. ArbabshiraniGregory J. MooreDaniel D’souzaSara Hooker
- Journals
- Journal of the American College of Cardiology (2 papers)European Heart Journal - Cardiovascular Imaging (1 paper)JACC. Cardiovascular imaging (1 paper)Cardiology (1 paper)Journal of Obesity (1 paper)
- Partner nations
- United StatesIndiaOman
In The Last Decade
Chirag Agarwal
38 papers receiving 411 citations
Peers
Comparison fields: 5 of 103
- Health Informatics 15
- Neurology 80
- Infectious Diseases 88
- Artificial Intelligence 118
- Computer Vision and Pattern Recognition 68
Countries citing papers authored by Chirag Agarwal
This map shows the geographic impact of Chirag Agarwal'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 Chirag Agarwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chirag Agarwal more than expected).
Fields of papers citing papers by Chirag Agarwal
This network shows the impact of papers produced by Chirag Agarwal. 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 Chirag Agarwal. The network helps show where Chirag Agarwal may publish in the future.
Co-authors
The 25 scholars most cited alongside Chirag Agarwal, 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 | 2026 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2023 | 62 | |
| 5 | 2023 | 12 | |
| 6 | 2022 | 3 | |
| 7 | 2022 | 6 | |
| 8 | 2022 | 7 | |
| 9 | 2021 | 7 | |
| 10 | Intriguing generalization and simplicity of adversarially trained neural networks | 2020 | 2 |
| 11 | 2020 | 2 | |
| 12 | 2019 | 10 | |
| 13 | 2017 | 0 | |
| 14 | 2017 | 9 | |
| 15 | 2017 | 1 | |
| 16 | 2017 | 24 | |
| 17 | 2016 | 15 | |
| 18 | 2015 | 2 | |
| 19 | 2015 | 6 | |
| 20 | 2014 | 6 |
About Chirag Agarwal
Chirag Agarwal is a scholar working on Health Informatics, Cardiology and Cardiovascular Medicine, Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 45 papers that have together received 424 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (5 papers), Cardiovascular Function and Risk Factors (3 papers), Cardiac Structural Anomalies and Repair (3 papers), COVID-19 diagnosis using AI (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Cardiac Valve Diseases and Treatments (3 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Cardiovascular Disease and Adiposity (2 papers). The work is most often cited by research in Health Informatics (15 citations), Neurology (80 citations), Infectious Diseases (88 citations), Artificial Intelligence (118 citations) and Computer Vision and Pattern Recognition (68 citations). Chirag Agarwal has collaborated with scholars based in United States, India and Oman. Frequent co-authors include Marinka Žitnik, Himabindu Lakkaraju, Dan Schonfeld, Anh‐Tu Nguyen, Mohammad R. Arbabshirani, Gregory J. Moore, Daniel D’souza, Sara Hooker, Jacob Shani and Ashutosh Gupta. Their work appears in journals such as Journal of the American College of Cardiology, European Heart Journal - Cardiovascular Imaging, JACC. Cardiovascular imaging, Cardiology and Journal of Obesity.
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.