I am presently an Assistant Professor in the Department of Data Sciences and Operations at USC Marshall Business School. Previously, I was a Stein Fellow/Lecturer in the Department of Statistics at Stanford University. Prior to Stanford, I was a postdoc 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. Before that I obtained 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. If you are interested in my research, feel free to send me an email.
I love to sketch. In my spare time I sometimes go on sketchcrawls. I enjoy being outdoors, and given any opportunity I like to find myself on a ridge or a peak or a pass or a lake.
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. Wu, Y. Yu. Online network change point detection with missing values. Arxiv preprint. [link]
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.
P. Dubey, Y. Chen, S. Bhattacharjee, A. Gajardo, C. Carroll, Y. Zhou, H. Chen and H.G. Müller. Learning delay dynamics for multivariate stochastic processes, with application to the prediction of the growth rate of COVID-19 cases in the United States. Journal of Mathematical Analysis and Applications. [link]
P. Dubey, Y. Chen, H.G. Müller. Depth profiles and the geometric exploration of random objects through optimal transport. Arxiv preprint. [link]
At USC Marshall
Spring 2022 BUAD 310 (Applied Business Statistics)
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