Harsha Nori
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education 2
- Safety Research top 5%
- Ethics and Social Impacts of AI 2
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI) 5
- Machine Learning in Healthcare 2
- Adversarial Robustness in Machine Learning 2
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- Pregnancy and preeclampsia studies 3
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- Maternal and fetal healthcare 2
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- Trauma and Emergency Care Studies 2
- Co-authors
- Rich CaruanaSamuel JenkinsJennifer Wortman VaughanHanna WallachHarmanpreet KaurMarco Túlio RibeiroSaleema AmershiNicholas S. P. King
- Journals
- American Journal of Obstetrics and Gynecology (4 papers)SHILAP Revista de lepidopterología (1 paper)Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2 papers)
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
Harsha Nori
12 papers receiving 332 citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Health Informatics 48
- Safety Research 98
- Artificial Intelligence 224
- Information Systems and Management 20
- Computer Science Applications 14
Countries citing papers authored by Harsha Nori
This map shows the geographic impact of Harsha Nori'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 Harsha Nori with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Harsha Nori more than expected).
Fields of papers citing papers by Harsha Nori
This network shows the impact of papers produced by Harsha Nori. 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 Harsha Nori. The network helps show where Harsha Nori may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Harsha Nori, 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 | 2024 | 6 | |
| 2 | 2023 | 1 | |
| 3 | 2023 | 2 | |
| 4 | 2023 | 1 | |
| 5 | 2023 | 4 | |
| 6 | 2023 | 3 | |
| 7 | 2023 | 32 | |
| 8 | 2022 | 3 | |
| 9 | 2022 | 10 | |
| 10 | Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learningbreakdown → | 2020 | 252 |
| 11 | 2020 | 15 | |
| 12 | 2018 | 18 |
About Harsha Nori
Harsha Nori is a scholar working on Health Informatics, Obstetrics and Gynecology and Safety Research, having authored 12 papers that have together received 347 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (5 papers), Pregnancy and preeclampsia studies (3 papers), Maternal and fetal healthcare (2 papers), Machine Learning in Healthcare (2 papers), Artificial Intelligence in Healthcare and Education (2 papers), Adversarial Robustness in Machine Learning (2 papers), Ethics and Social Impacts of AI (2 papers) and Trauma and Emergency Care Studies (2 papers). The work is most often cited by research in Health Informatics (48 citations), Safety Research (98 citations) and Artificial Intelligence (224 citations). Harsha Nori has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Rich Caruana, Samuel Jenkins, Jennifer Wortman Vaughan, Hanna Wallach, Harmanpreet Kaur, Marco Túlio Ribeiro, Saleema Amershi, Nicholas S. P. King, Bolin Ding and Joshua E. Allen. Their work appears in journals such as American Journal of Obstetrics and Gynecology, SHILAP Revista de lepidopterología, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Proceedings of the AAAI Conference on Artificial Intelligence and PubMed.
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.