Purnamrita Sarkar

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Andrew MooreDean, School of Computer Science, Carnegie MellonVerified email at cs.cmu.edu
Deepayan ChakrabartiAssociate Professor, McCombs School of Business, UT AustinVerified email at utexas.edu
peter bickelProfessor of Statistics, UC BerkeleyVerified email at stat.berkeley.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Michael FranklinUniversity of ChicagoVerified email at uchicago.edu
Bowei YanUniversity of Texas at AustinVerified email at utexas.edu
Xueyu Mao (毛学宇)The University of Texas at AustinVerified email at cs.utexas.edu
Soumendu Sundar MukherjeeAssistant Professor at Statistics and Mathematics Unit, Indian Statistical Institute, KolkataVerified email at isical.ac.in
Ameet TalwalkarCMU, DatadogVerified email at cmu.edu
Tim KraskaMITVerified email at mit.edu
Y. X. Rachel WangSchool of Mathematics and Statistics, University of SydneyVerified email at sydney.edu.au
Mingzhang YinAssistant Professor, University of FloridaVerified email at ufl.edu
Samuel MaddenMITVerified email at csail.mit.edu
Barzan MozafariAssociate Professor, University of Michigan (Ann Arbor)Verified email at umich.edu
Lizhen LinDepartment of Mathematics, The University of MarylandVerified email at umd.edu
Sharmodeep BhattacharyyaAssistant Professor of Statistics, Oregon State UniversityVerified email at science.oregonstate.edu
Tianxi LiAssistant Professor of Statistics, University of Minnesota, Twin CitiesVerified email at umn.edu
Koen Van den BergeJ&J Innovative MedicineVerified email at berkeley.edu
LIHUA LEIStanford UniversityVerified email at stanford.edu
Elizaveta (Liza) LevinaProfessor of Statistics, University of MichiganVerified email at umich.edu

Purnamrita Sarkar

Associate Professor, Statistics and Data Sciences, University of Texas at Austin

Verified email at austin.utexas.edu

Theoretical Statistics Scalable Algorithms Networks Machine Learning Random Matrices


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A scalable bootstrap for massive data A Kleiner, A Talwalkar, P Sarkar, MI Jordan Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2014	520	2014
Dynamic social network analysis using latent space models P Sarkar, AW Moore Acm sigkdd explorations newsletter 7 (2), 31-40, 2005	467	2005
Scaling up crowd-sourcing to very large datasets: a case for active learning B Mozafari, P Sarkar, M Franklin, M Jordan, S Madden Proceedings of the VLDB Endowment 8 (2), 125-136, 2014	288*	2014
Hypothesis testing for automated community detection in networks PJ Bickel, P Sarkar Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2016	225	2016
Nonparametric link prediction in large scale dynamic networks P Sarkar, D Chakrabarti, M Jordan	222*	2014
The big data bootstrap A Kleiner, A Talwalkar, P Sarkar, M Jordan arXiv preprint arXiv:1206.6415, 2012	182	2012
Role of normalization in spectral clustering for stochastic blockmodels P Sarkar, PJ Bickel	143	2015
Dynamic social network analysis using latent space models P Sarkar, AW Moore Advances in neural information processing systems 18, 1145, 2006	138	2006
Theoretical Justification of Popular Link Prediction Heuristics. P Sarkar, D Chakrabarti, AW Moore IJCAI proceedings-international joint conference on artificial intelligence …, 2011	135	2011
Fast incremental proximity search in large graphs P Sarkar, AW Moore, A Prakash Proceedings of the 25th international conference on Machine learning, 896-903, 2008	133	2008
Crowdsourced enumeration queries B Trushkowsky, T Kraska, MJ Franklin, P Sarkar 2013 IEEE 29th international conference on data engineering (ICDE), 673-684, 2013	130	2013
Estimating mixed memberships with sharp eigenvector deviations X Mao, P Sarkar, D Chakrabarti Journal of the American Statistical Association 116 (536), 1928-1940, 2021	112*	2021
A tractable approach to finding closest truncated-commute-time neighbors in large graphs P Sarkar, A Moore arXiv preprint arXiv:1206.5259, 2012	107	2012
A latent space approach to dynamic embedding of co-occurrence data P Sarkar, SM Siddiqi, GJ Gordon Artificial Intelligence and Statistics, 420-427, 2007	90	2007
Hierarchical community detection by recursive partitioning T Li, L Lei, S Bhattacharyya, K Van den Berge, P Sarkar, PJ Bickel, ... Journal of the American Statistical Association 117 (538), 951-968, 2022	89	2022
Covariate regularized community detection in sparse graphs B Yan, P Sarkar Journal of the American Statistical Association 116 (534), 734-745, 2021	84*	2021
Fast nearest-neighbor search in disk-resident graphs P Sarkar, AW Moore Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010	74	2010
Convergence of gradient EM on multi-component mixture of Gaussians B Yan, M Yin, P Sarkar Advances in Neural Information Processing Systems 30, 2017	70*	2017
On mixed memberships and symmetric nonnegative matrix factorizations X Mao, P Sarkar, D Chakrabarti International conference on machine learning, 2324-2333, 2017	61	2017
On clustering network-valued data SS Mukherjee, P Sarkar, L Lin Advances in neural information processing systems 30, 2017	55	2017

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