Nina Mishra
- Artificial Intelligence top 1%
- Computer Networks and Communications top 5%
- Information Systems top 5%
- Sociology and Political Science top 10%
- Signal Processing top 5%
- Co-authors
- Tom FawcettKrishnaram KenthapadiSudipto GuhaLeonard PittAleksandra KorolovaAlexandros NtoulasKobbi NissimGourav Roy
- Topics
- Machine Learning and Algorithms (8 papers)Cryptography and Data Security (8 papers)Privacy-Preserving Technologies in Data (8 papers)
- Journals
- SHILAP Revista de lepidopterologíaArtificial IntelligenceMachine Learning
- Partner nations
- United StatesIsraelIndia
In The Last Decade
Nina Mishra
36 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 839
- Computer Networks and Communications 233
- Information Systems 188
- Sociology and Political Science 164
- Signal Processing 147
Countries citing papers authored by Nina Mishra
This map shows the geographic impact of Nina Mishra'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 Nina Mishra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nina Mishra more than expected).
Fields of papers citing papers by Nina Mishra
This network shows the impact of papers produced by Nina Mishra. 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 Nina Mishra. The network helps show where Nina Mishra may publish in the future.
Co-authorship network of co-authors of Nina Mishra
This figure shows the co-authorship network connecting the top 25 collaborators of Nina Mishra. A scholar is included among the top collaborators of Nina Mishra 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 Nina Mishra. Nina Mishra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Semi-Supervised Learning on Data Streams via Temporal Label Propagation | 22 |
| 2 | 7 | |
| 3 | 2 | |
| 4 | 15 | |
| 5 | Predicting Consumer Behavior in Commerce Search. | 4 |
| 6 | 38 | |
| 7 | 3 | |
| 8 | 28 | |
| 9 | 44 | |
| 10 | 44 | |
| 11 | 81 | |
| 12 | Enabling Privacy for the Paranoids | 6 |
| 13 | 8 | |
| 14 | 1 | |
| 15 | Proceedings, Twentieth International Conference on Machine Learning | 233 |
| 16 | 60 | |
| 17 | 34 | |
| 18 | Version spaces without boundary sets | 17 |
| 19 | 9 | |
| 20 | 25 |
About Nina Mishra
Nina Mishra is a scholar working on Computer Science Applications, Artificial Intelligence and Health Informatics, having authored 37 papers that have together received 1.2k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (8 papers), Cryptography and Data Security (8 papers) and Privacy-Preserving Technologies in Data (8 papers). The work is most often cited by research in Artificial Intelligence (839 citations), Computer Science Applications (83 citations) and Signal Processing (147 citations). Nina Mishra has collaborated with scholars based in United States, Israel and India. Frequent co-authors include Tom Fawcett, Krishnaram Kenthapadi, Sudipto Guha, Leonard Pitt, Aleksandra Korolova, Alexandros Ntoulas, Kobbi Nissim, Gourav Roy, Okke Schrijvers and Samuel Ieong. Their work appears in journals such as SHILAP Revista de lepidopterología, Artificial Intelligence and Machine Learning.
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.