Debdeep Pati

1.6k total citations
46 papers, 714 citations indexed

About

Debdeep Pati is a scholar working on Artificial Intelligence, Statistics and Probability and Geometry and Topology. According to data from OpenAlex, Debdeep Pati has authored 46 papers receiving a total of 714 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 33 papers in Statistics and Probability and 4 papers in Geometry and Topology. Recurrent topics in Debdeep Pati's work include Statistical Methods and Inference (27 papers), Bayesian Methods and Mixture Models (26 papers) and Statistical Methods and Bayesian Inference (17 papers). Debdeep Pati is often cited by papers focused on Statistical Methods and Inference (27 papers), Bayesian Methods and Mixture Models (26 papers) and Statistical Methods and Bayesian Inference (17 papers). Debdeep Pati collaborates with scholars based in United States, Australia and United Kingdom. Debdeep Pati's co-authors include Anirban Bhattacharya, David B. Dunson, Natesh S. Pillai, Yun Yang, Brian J. Reich, Bani K. Mallick, Surya T. Tokdar, Xiaoning Qian, Tanner Kirk and Raymundo Arróyave and has published in prestigious journals such as Journal of the American Statistical Association, Technometrics and Biometrics.

In The Last Decade

Debdeep Pati

38 papers receiving 699 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Debdeep Pati United States	13	350	319	64	50	38	46	714
Anirban Bhattacharya United States	14	427 1.2×	390 1.2×	72 1.1×	91 1.8×	27 0.7×	44	906
Victor M. Panaretos Switzerland	10	214 0.6×	181 0.6×	72 1.1×	53 1.1×	28 0.7×	39	767
Adam M. Johansen United Kingdom	14	212 0.6×	346 1.1×	25 0.4×	80 1.6×	17 0.4×	42	709
R. Frühwirth Austria	18	185 0.5×	257 0.8×	43 0.7×	25 0.5×	67 1.8×	119	1.1k
Shaojun Guo China	14	380 1.1×	139 0.4×	73 1.1×	38 0.8×	16 0.4×	33	741
Pushpa N. Rathie Brazil	14	172 0.5×	138 0.4×	112 1.8×	31 0.6×	36 0.9×	91	735
Matti Vihola Finland	10	166 0.5×	204 0.6×	33 0.5×	28 0.6×	17 0.4×	31	454
Zhongyi Zhu China	20	998 2.9×	246 0.8×	167 2.6×	24 0.5×	34 0.9×	109	1.3k
Brett Presnell United States	13	705 2.0×	361 1.1×	48 0.8×	92 1.8×	59 1.6×	37	1.3k
Tamás F. Móri Hungary	13	196 0.6×	132 0.4×	17 0.3×	25 0.5×	27 0.7×	77	831

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-authorship network of co-authors of Debdeep Pati

This figure shows the co-authorship network connecting the top 25 collaborators of Debdeep Pati. A scholar is included among the top collaborators of Debdeep Pati 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 Debdeep Pati. Debdeep Pati is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Yang, Xiaojun, et al.. (2025). Modeling Hurricane Vulnerability in a Large Coastal City Using Bayesian Gaussian Spatial Regression. Annals of the American Association of Geographers. 116(3). 561–581.

Zhao, Peng, et al.. (2024). Tail-adaptive Bayesian shrinkage. Electronic Journal of Statistics. 18(2).

Kirk, Tanner, Anirban Bhattacharya, Debdeep Pati, et al.. (2021). Bayesian optimization with adaptive surrogate models for automated experimental design. npj Computational Materials. 7(1). 79 indexed citations

Lim, Yeunhwan, Anirban Bhattacharya, Jeremy W. Holt, & Debdeep Pati. (2020). Radius and equation of state constraints from massive neutron stars and GW190814. arXiv (Cornell University). 17 indexed citations

Pati, Debdeep, et al.. (2020). Modality-Constrained Density Estimation via Deformable Templates. Technometrics. 63(4). 536–547.

Pati, Debdeep, et al.. (2020). Bayesian graph selection consistency under model misspecification. Bernoulli. 27(1). 637–672. 3 indexed citations

Lipsitz, Stuart R., et al.. (2019). A New Bayesian Single Index Model with or without Covariates Missing at Random. Bayesian Analysis. 15(3). 759–780. 5 indexed citations

Ghosh, Prasenjit, Debdeep Pati, & Anirban Bhattacharya. (2019). Posterior Contraction Rates for Stochastic Block Models. Sankhya A. 82(2). 448–476. 2 indexed citations

Pati, Debdeep, Anirban Bhattacharya, & Yun Yang. (2018). On Statistical Optimality of Variational Bayes. International Conference on Artificial Intelligence and Statistics. 1579–1588. 4 indexed citations

10.

Bhattacharya, Anirban, Debdeep Pati, & Yun Yang. (2018). Bayesian fractional posteriors. The Annals of Statistics. 47(1). 39 indexed citations

11.

Pati, Debdeep, et al.. (2017). A Geometric Framework For Density Modeling. arXiv (Cornell University). 1 indexed citations

12.

Tang, Yuanyuan, Debajyoti Sinha, Debdeep Pati, Stuart R. Lipsitz, & Steven E. Lipshultz. (2015). Bayesian partial linear model for skewed longitudinal data. Biostatistics. 16(3). 441–453. 8 indexed citations

13.

Pati, Debdeep & Anirban Bhattacharya. (2015). Adaptive Bayesian inference in the Gaussian sequence model using exponential-variance priors. Statistics & Probability Letters. 103. 100–104. 4 indexed citations

14.

Sarkar, Abhra, Bani K. Mallick, John Staudenmayer, Debdeep Pati, & Raymond J. Carroll. (2014). Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors. Journal of Computational and Graphical Statistics. 23(4). 1101–1125. 13 indexed citations

15.

Bhattacharya, Anirban, et al.. (2014). Anisotropic function estimation using multi-bandwidth Gaussian processes. The Annals of Statistics. 42(1). 352–381. 27 indexed citations

16.

Pati, Debdeep, Anirban Bhattacharya, & Guang Cheng. (2014). Optimal Bayesian estimation in random covariate design with a rescaled Gaussian process prior. Journal of Machine Learning Research. 16(1). 2837–2851. 3 indexed citations

17.

Pati, Debdeep, David B. Dunson, & Surya T. Tokdar. (2013). Posterior consistency in conditional distribution estimation. Journal of Multivariate Analysis. 116. 456–472. 39 indexed citations

18.

Pati, Debdeep & David B. Dunson. (2013). Bayesian nonparametric regression with varying residual density. Annals of the Institute of Statistical Mathematics. 66(1). 1–31. 10 indexed citations

19.

Bhattacharya, Anirban, Debdeep Pati, & David B. Dunson. (2011). Adaptive dimension reduction with a Gaussian process prior. arXiv (Cornell University). 2 indexed citations

20.

Pati, Debdeep, Brian J. Reich, & David B. Dunson. (2011). Bayesian geostatistical modelling with informative sampling locations. Biometrika. 98(1). 35–48. 58 indexed citations

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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