I am presently a Stein Fellow/Lecturer in the Department of Statistics at Stanford University. Previously, I was a Postdoctoral researcher working with Prof. Hans-Georg Müller, who is also my PhD advisor, and Prof. Jane-Ling Wang in the Department of Statistics at University of California, Davis. I finished my PhD in June 2019. Prior to this, I received a Bachelor of Statistics in 2012 and a Master of Statistics in 2014 from the Indian Statistical Institute, Kolkata.
My research centers around developing statistical methods for nonEuclidean data, examples being distribution and network valued data and time varying object data. Here is a link to my CV. In June 2021, I will join as an Assistant Professor in the Department of Data Sciences and Operations at USC Marshall Business School.
I love to sketch. In my spare time I sometimes go on sketchcrawls. I also love hiking.
Modeling and inference for dynamic networks- generating mechanisms and reconstruction of network archeology.
Interface of statistics and metric geometry - broadly applicable statistical methods, theory and inference for analyzing metric space valued data with applications in brain imaging studies, child neurological development, traffic network analysis, social network analysis, genetics and compositional data.
Functional and longitudinal data analysis and its overlap with metric geometry- with focus on studying samples of dynamic metric space data, examples being time varying networks, distributions or tree valued data.
Online learning problems- specifically contextual multi-armed bandit problems with unconventional contexts.
Publications and Preprints
P. Dubey, H.G. Müller. Modeling Time-Varying Random Objects and Dynamic Networks. Journal of the American Statistical Association. [link]
Y. Chen*, P. Dubey*, H.G. Müller*, M. Bruchhage, J.L. Wang, S. Deoni and RESONANCE Consortium. Modeling Sparse Longitudinal Data in Early Neurodevelopment. Neuroimage (Special Issue on Longitudinal Neuroimaging). [link]
C. Carroll, S. Bhattacharjee, Y. Chen, P. Dubey, J. Fan, Á. Gajardo, X. Zhou, H.G. Müller and J.L. Wang. Time Dynamics of COVID-19. Scientific Reports. [link]
P. Dubey, H.G. Müller. Fréchet Change Point Detection. Annals of Statistics.[link]
P. Dubey, H.G. Müller. Functional Models for Time Varying Object Data. Journal of Royal Statistical Society: Series B (With discussion).[link]
P. Dubey, H.G. Müller. Fréchet Analysis of Variance for Random Objects. Preprint available as arXiv:1710.02761. Biometrika, winner of Best Paper Awards at JSM, 2018 (Section of Nonparametric Statistics) and 2018 IISA International Conference on Statistics. [link]
P. Sur, G. Shmueli, S. Bose, and P. Dubey. Modeling bimodal discrete data using Conway- Maxwell-Poisson mixture models. Journal of Business and Economic Statistics, Volume 33, 2015 - Issue 3.[link]
S. Bose, G. Shmueli, P. Sur, and P. Dubey. Fitting COM-Poisson mixtures to bimodal count data. Proceedings of the 2013 International Conference on Information, Operations Management and Statistics (ICIOMS 2013), winner of Best Paper Award.
At Stanford University
Winter 2021 STATS/BIO 141-Biostatistics
Fall 2020 STATS 116- Theory of Probability
At UC Davis
Spring 2019 STAT 131A-Introduction to Probability Theory
Summer 2018 STAT 13- Elementary Statistics
IMS New Researcher Travel Award 2020
Peter Hall Graduate Research Award 2019, awarded for outstanding graduate research
SF Bay Area Chapter of ASA Travel Award for Joint Statistical Meetings 2019 for "Modeling Time Varying Object Data"
Student Paper Award, International Indian Statistical Association Conference, 2018 and Best Paper Award, Section on Nonparametric Statistics, Joint Statistical Meetings, 2018 for "Fréchet Analysis of Variance for Random Objects"
UC Davis and Humanities Graduate Research Award, 2018
UC Davis Graduate Studies Travel Award, 2018
Summer Statistics Research Fellowship, UC Davis, 2015-2017
Get in Touch
Department of Statistics
390 Serra Mall
Stanford, CA 94305