Debdeep Pati

1.6k citations
46 papers · 714 · h-index 13

Impact in

Papers in

    • Bayesian Methods and Mixture Models 26
    • Gaussian Processes and Bayesian Inference 12
    • Target Tracking and Data Fusion in Sensor Networks 3
    • Statistical Methods and Inference 27
    • Statistical Methods and Bayesian Inference 17

Debdeep Pati

38 papers receiving 699 citations

Peers

Debdeep Pati
Comparison fields: 5 of 98
  • Statistics and Probability 350
  • Computational Mathematics 6
  • Artificial Intelligence 319
  • Statistics, Probability and Uncertainty 38
  • Ecological Modeling 16
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Citations per field
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Citations per year

Countries citing papers authored by Debdeep Pati

Since Specialization
Citations

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

Fields of papers citing papers by Debdeep Pati

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014205
2 202179
3 201158
4 201842
5 201339
6 201839
7 201630
8 201427
9 201924
10 202022
11 202017
12 201517
13 201413
14 201912
15 201712
16 201310
17 20158
18 20147
19 20195
20
On Statistical Optimality of Variational Bayes
20184

About Debdeep Pati

Debdeep Pati is a scholar working on Artificial Intelligence, Statistics and Probability, Geometry and Topology, Molecular Biology and Mathematical Physics, having authored 46 papers that have together received 714 indexed citations. Recurring topics across this work include Statistical Methods and Inference (27 papers), Bayesian Methods and Mixture Models (26 papers), Statistical Methods and Bayesian Inference (17 papers), Gaussian Processes and Bayesian Inference (12 papers), Morphological variations and asymmetry (4 papers), Target Tracking and Data Fusion in Sensor Networks (3 papers), Genetic and phenotypic traits in livestock (2 papers) and Medical Image Segmentation Techniques (2 papers). The work is most often cited by research in Statistics and Probability (350 citations), Computational Mathematics (6 citations), Artificial Intelligence (319 citations), Statistics, Probability and Uncertainty (38 citations) and Ecological Modeling (16 citations). Debdeep Pati has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Anirban Bhattacharya, David B. Dunson, Natesh S. Pillai, Yun Yang, Brian J. Reich, Bani K. Mallick, Surya T. Tokdar, Tanner Kirk, Raymundo Arróyave and Xiaoning Qian. Their work appears in journals such as Journal of the American Statistical Association, The Annals of Statistics, Journal of Computational and Graphical Statistics, Bayesian Analysis and npj Computational Materials.

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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