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School of Mathematical Sciences

Dr Prajamitra Bhuyan

Prajamitra

Lecturer in Mathematical Data Science

Email: p.bhuyan@qmul.ac.uk
Room Number: Mathematical Sciences Building, Room: MB-B24
Website: https://prajamitrabhuyan.wixsite.com/profile
Office Hours: Tuesdays 12:30 -13:30. Just click on the following link to enter my virtual office during this time: https://whereby.com/prajamitra-bhuyan

Profile

I am a Lecturer in Mathematical Data Science at the Queen Mary University of London. I am also engaged with the data-centric engineering programme at The Alan Turing Institute. Before joining Queen Mary University London, I held a postdoctoral position at the Imperial College London and National Postdoctoral Fellowship in India. I obtained PhD degree in Statistics from the Indian Statistical Institute. I have several years of industry experience as a Data Scientist in the Analytics sector providing training and analytical solutions to global clients across industry verticals. I am passionate about solving real-world business problems and issues related to the humanitarian crisis.

Teaching

I am currently teaching Probability & Statistics II (MTH5129).

Research

Research Interests:

My primary research interests lie broadly in statistical data science and methodology, motivated by real-life challenges arising from complex systems, social science and public policy. In particular, my doctoral work deals with computational and inferential issues in time-dependent stress-strength interference. In postdoctoral research, I worked on modeling and analysis of incomplete longitudinal data with missingness and zero-inflation. Currently, I am working on causal inference and its application in transport networks. I am also engaged in cross-disciplinary work, focusing on data-analytic settings in sports and environmental sciences.

Publications

  • Bhuyan P, McCoy EJ, Li H et al. (2021). Analysing the causal effect of London cycle superhighways on traffic congestion The Annals of Applied Statistics.
  • Jha J, Bhuyan P (2021). Two-stage circular-circular regression with zero inflation: Application to medical sciences Annals of Applied Statistics.
  • Nanda P, Bhuyan P, Dewanji A (2021). Optimal replacement policy under cumulative damage model and strength degradation with applications Annals of Operations Research.
  • Chatterjee K, Bhuyan P (2020). On the estimation of population size from a post-stratified two-sample capture–recapture data under dependence Journal of Statistical Computation and Simulation.
  • Chattopadhyay N, Bhuyan P (2020). Player selection strategy: a quantitative perspective Proceedings of the 62nd ISI World Statistics Congresses: Contributed Paper Session, Vol-3.
  • Jha P, Banerjee S, Bhuyan P et al. (2020). Elemental distribution in urban sediments of small waterbodies and its implications: a case study from Kolkata, India Environmental Geochemistry and Health.
  • Bhuyan P, Ghosh S, Majumder P et al. (2020). A bivariate life distribution and notions of negative dependence Stat.
  • Bhuyan P (2019). Estimation of random-effects model for longitudinal data with nonignorable missingness using Gibbs sampling Computational Statistics.
  • Chatterjee K, Bhuyan P (2019). On the estimation of population size from a dependent triple-record system Journal of the Royal Statistical Society. Series A: Statistics in Society.
  • Bhuyan P, Biswas J, Ghosh P et al. (2019). A Bayesian two-stage regression approach of analysing longitudinal outcomes with endogeneity and incompleteness Statistical Modelling.
  • Bhuyan P, Mitra M, Dewanji A (2018). Identifiability issues in dynamic stress–strength modeling Annals of the Institute of Statistical Mathematics.
  • Bhuyan P, Sengupta D (2017). Estimation of reliability with semi-parametric modeling of degradation Computational Statistics and Data Analysis.
  • Bhuyan P, Dewanji A (2017). Estimation of reliability with cumulative stress and strength degradation Statistics.
  • Bhuyan P, Dewanji A (2017). Reliability computation under dynamic stress–strength modeling with cumulative stress and strength degradation Communications in Statistics: Simulation and Computation.