Rohan Taori
Impact in
-
- Artificial Intelligence in Healthcare and Education
-
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Topic Modeling
- Anomaly Detection Techniques and Applications
Papers in ⓘ
-
- Anomaly Detection Techniques and Applications 2
- Domain Adaptation and Few-Shot Learning 2
- Neural Networks and Applications 1
- Machine Learning and Data Classification 1
-
- COVID-19 diagnosis using AI 2
- Co-authors
- Ludwig Schmidt (4 shared papers)Inioluwa Deborah Raji (1 shared paper)Vaishaal Shankar (3 shared papers)Nicholas Carlini (2 shared papers)Achal Dave (2 shared papers)Benjamin Recht (2 shared papers)Pang Wei Koh (1 shared paper)Aditi Raghunathan (1 shared paper)
- Journals
- Neural Information Processing Systems (2 papers)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesSwitzerlandIsrael
In The Last Decade
Rohan Taori
5 papers receiving 40 citations
Peers
Comparison fields: 5 of 29
- Health Informatics 5
- Artificial Intelligence 29
- Computer Vision and Pattern Recognition 16
- Safety Research 6
- Information Systems and Management 2
Countries citing papers authored by Rohan Taori
This map shows the geographic impact of Rohan Taori'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 Rohan Taori with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rohan Taori more than expected).
Fields of papers citing papers by Rohan Taori
This network shows the impact of papers produced by Rohan Taori. 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 Rohan Taori. The network helps show where Rohan Taori may publish in the future.
Co-authors
The 19 scholars most cited alongside Rohan Taori, 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 | Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning | 2021 | 17 |
| 2 | 2021 | 13 | |
| 3 | Measuring Robustness to Natural Distribution Shifts in Image Classification | 2020 | 11 |
| 4 | When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet | 2019 | 2 |
| 5 | 2025 | 1 |
About Rohan Taori
Rohan Taori is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Infectious Diseases, Organic Chemistry and Surgery, having authored 5 papers that have together received 44 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (2 papers), Anomaly Detection Techniques and Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Neural Networks and Applications (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Health Informatics (5 citations), Artificial Intelligence (29 citations), Computer Vision and Pattern Recognition (16 citations), Safety Research (6 citations) and Information Systems and Management (2 citations). Rohan Taori has collaborated with scholars based in United States, Switzerland and Israel. Frequent co-authors include Ludwig Schmidt, Inioluwa Deborah Raji, Vaishaal Shankar, Nicholas Carlini, Achal Dave, Benjamin Recht, Pang Wei Koh, Aditi Raghunathan, Shiori Sagawa and Percy Liang. Their work appears in journals such as Neural Information Processing Systems and arXiv (Cornell University).
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.