Nina Mishra

2.6k total citations
37 papers, 1.2k citations indexed

About

Nina Mishra is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Information Systems. According to data from OpenAlex, Nina Mishra has authored 37 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 6 papers in Statistical and Nonlinear Physics and 5 papers in Information Systems. Recurrent topics in Nina Mishra's work include Machine Learning and Algorithms (8 papers), Cryptography and Data Security (8 papers) and Privacy-Preserving Technologies in Data (8 papers). Nina Mishra is often cited by papers focused on Machine Learning and Algorithms (8 papers), Cryptography and Data Security (8 papers) and Privacy-Preserving Technologies in Data (8 papers). Nina Mishra collaborates with scholars based in United States, Israel and India. Nina Mishra's co-authors include Tom Fawcett, Krishnaram Kenthapadi, Sudipto Guha, Leonard Pitt, Aleksandra Korolova, Alexandros Ntoulas, Kobbi Nissim, Gourav Roy, Okke Schrijvers and Samuel Ieong and has published in prestigious journals such as SHILAP Revista de lepidopterología, Artificial Intelligence and Machine Learning.

In The Last Decade

Nina Mishra

36 papers receiving 1.1k citations

Peers

Nina Mishra
Comparison fields: 5 of 115
  • Artificial Intelligence 839
  • Computer Networks and Communications 233
  • Information Systems 188
  • Sociology and Political Science 164
  • Signal Processing 147
Replace T-H. Hubert Chan with:
T-H. Hubert Chan Hong Kong
H. Kargupta United States
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Bert Huang United States
Tamir Tassa Israel
Shiva Prasad Kasiviswanathan United States
András A. Benczúr Hungary
T-H. Hubert Chan Hong Kong View profile →
Citations per field, relative to Nina Mishra
Nina Mishra · 1×
Citations per year, relative to Nina Mishra
Nina Mishra · 1×

Countries citing papers authored by Nina Mishra

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
# 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

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.

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