Puja Das

434 citations
30 papers · 199 · h-index 8

Impact in

Papers in

Puja Das

21 papers receiving 189 citations

Peers

Puja Das
Comparison fields: 5 of 75
  • Management Science and Operations Research 75
  • Finance 37
  • Issues, ethics and legal aspects 4
  • Computer Networks and Communications 61
  • Information Systems 37
Replace Manish P. Kurhekar with:
Manish P. Kurhekar India
Kaiyu Huang China
Rasmita Rautray India
Jingyu He China
Yu. S. Kharin Belarus
Seung Hyong Rhee South Korea
Eva Armengol Spain
Sachin Agarwal Germany
Ga Wu Canada
Puja Das relative to Manish P. Kurhekar India Manish P. Kurhekar's profile →
Citations per field
00.5×1.5×
Manish P. Kurhekar · 1×
Citations per year

Countries citing papers authored by Puja Das

Since Specialization
Citations

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

Fields of papers citing papers by Puja Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Puja Das, 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 Puja Das Line = papers co-authored together Puja Das links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201337
2 201131
3 201926
4 201017
5 201416
6 20198
7 20237
8 20237
9 20217
10 20246
11 20176
12 20235
13 20244
14
Safe design of a composite structure - a stochastic approach
20064
15 20243
16 20193
17 20152
18 20242
19 20242
20
Online Quadratically Constrained Convex Optimization with Applications to Risk Adjusted Portfolio Selection
20122

About Puja Das

Puja Das is a scholar working on Information Systems, Computer Networks and Communications, Artificial Intelligence, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 30 papers that have together received 199 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (5 papers), Blockchain Technology Applications and Security (4 papers), IoT and Edge/Fog Computing (4 papers), Cloud Computing and Resource Management (2 papers), Caching and Content Delivery (2 papers), AI in cancer detection (2 papers), Sperm and Testicular Function (2 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). The work is most often cited by research in Management Science and Operations Research (75 citations), Finance (37 citations), Issues, ethics and legal aspects (4 citations), Computer Networks and Communications (61 citations) and Information Systems (37 citations). Puja Das has collaborated with scholars based in India, United States and Czechia. Frequent co-authors include Arindam Banerjee, Nicholas D. Johnson, Deepsubhra Guha Roy, Debashis De, Rajkumar Buyya, Karen A. Monsen, Jackson Durairaj Selvan Christyraj, Biswaranjan Acharya, Kamarajan Rajagopalan and D.A. Karras. Their work appears in journals such as APOPTOSIS, Journal of Thermal Biology, Journal of Network and Computer Applications, Reproductive Sciences and International Journal of Intelligent Systems.

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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