Mandis Beigi

630 citations
18 papers · 373 · h-index 9

Impact in

Papers in

Mandis Beigi

17 papers receiving 323 citations

Peers

Mandis Beigi
Comparison fields: 5 of 55
  • Computer Vision and Pattern Recognition 209
  • Computer Networks and Communications 102
  • Information Systems 74
  • Signal Processing 32
  • Artificial Intelligence 70
Replace Jean-Luc Meunier with:
Jean-Luc Meunier France
Abdul Monem S. Rahma Iraq
Eric Cole
Shi-Kuo Chang United States
Carsten Saathoff Germany
Utz Westermann Germany
Mehreen Afzal Pakistan
Dan Chalmers United Kingdom
W.I. Grosky United States
B. Thuraisingham United States
Mandis Beigi relative to Jean-Luc Meunier France Jean-Luc Meunier's profile →
Citations per field
00.5×1.5×1.8×
Jean-Luc Meunier · 1×
Citations per year

Countries citing papers authored by Mandis Beigi

Since Specialization
Citations

This map shows the geographic impact of Mandis Beigi'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 Mandis Beigi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mandis Beigi more than expected).

Fields of papers citing papers by Mandis Beigi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mandis Beigi. 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 Mandis Beigi. The network helps show where Mandis Beigi may publish in the future.

Co-authors

The 25 scholars most cited alongside Mandis Beigi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mandis Beigi Line = papers co-authored together Mandis Beigi links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 1997140
2 199749
3 200441
4 199835
5 200726
6 200516
7 200514
8 200814
9 20119
10 20037
11 20097
12
IPSECvalidate: A Tool to Validate {IPSEC} Configurations
20013
13 20103
14 20093
15 20242
16
Simulants: Synthetic Clinical Trial Data via Subject-Level Privacy-Preserving Synthesis.
20222
17 19992
18 20250

About Mandis Beigi

Mandis Beigi is a scholar working on Computer Networks and Communications, Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science and Information Systems, having authored 18 papers that have together received 373 indexed citations. Recurring topics across this work include Video Analysis and Summarization (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Image Retrieval and Classification Techniques (4 papers), Anomaly Detection Techniques and Applications (3 papers), Peer-to-Peer Network Technologies (3 papers), Service-Oriented Architecture and Web Services (2 papers), Access Control and Trust (2 papers) and Advanced Data Storage Technologies (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (209 citations), Computer Networks and Communications (102 citations), Information Systems (74 citations), Signal Processing (32 citations) and Artificial Intelligence (70 citations). Mandis Beigi has collaborated with scholars based in United States, Brazil and India. Frequent co-authors include Shih‐Fu Chang, Ana B. Benitez, John R. Smith, Dinesh Verma, Seraphin Calo, Dakshi Agrawal, Chatschik Bisdikian, Kang‐Won Lee, Upendra Sharma and Rohit Jain. Their work appears in journals such as Computer Standards & Interfaces, IBM Journal of Research and Development, IEEE Internet Computing, Blood and Communications of the ACM.

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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