Shreya Shankar
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- Cognitive Neuroscience
- Computer Networks and Communications
- Co-authors
- Aditya ParameswaranBrian CheungGamaleldin F. ElsayedJascha Sohl‐DicksteinNicolas PapernotIan GoodfellowJ.D. Zamfirescu-PereiraА.В. Куракин
- Topics
- Adversarial Robustness in Machine Learning (4 papers)Scientific Computing and Data Management (4 papers)Data Quality and Management (3 papers)
- Partner nations
- United StatesIndiaHong Kong
In The Last Decade
Shreya Shankar
16 papers receiving 200 citations
Hit Papers
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 104
- Computer Vision and Pattern Recognition 61
- Information Systems 23
- Cognitive Neuroscience 16
- Computer Networks and Communications 13
Countries citing papers authored by Shreya Shankar
This map shows the geographic impact of Shreya Shankar'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 Shreya Shankar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shreya Shankar more than expected).
Fields of papers citing papers by Shreya Shankar
This network shows the impact of papers produced by Shreya Shankar. 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 Shreya Shankar. The network helps show where Shreya Shankar may publish in the future.
Co-authorship network of co-authors of Shreya Shankar
This figure shows the co-authorship network connecting the top 25 collaborators of Shreya Shankar. A scholar is included among the top collaborators of Shreya Shankar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shreya Shankar. Shreya Shankar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 6 | |
| 3 | Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferencesbreakdown → | 51 |
| 4 | 10 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 4 | |
| 8 | 3 | |
| 9 | 7 | |
| 10 | 10 | |
| 11 | 3 | |
| 12 | 1 | |
| 13 | Adversarial Examples that Fool both Human and Computer Vision | 11 |
| 14 | 67 | |
| 15 | 7 | |
| 16 | Survey on passive methods of image tampering detection | 26 |
About Shreya Shankar
Shreya Shankar is a scholar working on Information Systems and Management, Computer Science Applications and Management Science and Operations Research, having authored 16 papers that have together received 212 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (4 papers), Scientific Computing and Data Management (4 papers) and Data Quality and Management (3 papers). The work is most often cited by research in Artificial Intelligence (104 citations), Computer Vision and Pattern Recognition (61 citations) and Health Informatics (4 citations). Shreya Shankar has collaborated with scholars based in United States, India and Hong Kong. Frequent co-authors include Aditya Parameswaran, Brian Cheung, Gamaleldin F. Elsayed, Jascha Sohl‐Dickstein, Nicolas Papernot, Ian Goodfellow, J.D. Zamfirescu-Pereira, А.В. Куракин, Ian Arawjo and Bjoern Hartmann. Their work appears in journals such as Nature Communications, Proceedings of the VLDB Endowment and Journal of Vision.
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.