Home

Biography

Personal Details:

I am an Assistant Professor in Statistics at the Indian Institute of Management, Calcutta. Prior to joining IIM Calcutta, I held a Lecturership in Mathematical Data Science at the Queen Mary University of London. Also, I was engaged with the data-centric engineering programme at the Alan Turing Institute. I held a postdoctoral position at the Imperial College London and National Postdoctoral Fellowship in India after completing 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.

Website:

prajamitrabhuyan.wixsite.com/profile

Academics

Academic Background:

PhD in Statistics, Indian Statistical Institute 
M.Stat, Indian Statistical Institute

Courses Taught:

Statistics for Managers (MBA). Managerial Statistics (MBAEX). Decision Making Tools (PGPEX-VLM). Bayesian Methods for Business Analytics (PGDBA). Advanced Mathematics (Doctoral). Advanced Probability (Doctoral). Stochastic Processes (Doctoral). Statistics for Finance (Long Duration Programs). Statistics for HR Analytics (Long Duration Programs, Management Development Program)

 

Awards:

• Elected Member since 2019, International Statistical Institute. 
• M. N. Murthy Memorial Prize : 2017-2018, awarded by Indian Statistical Institute for outstanding thesis in Applied Statistics. 
• National Postdoctoral Fellowship, 2017, Science and Engineering Research Board, Government of India. 
• Junior Research Fellowship, 2011, Indian Statistical Institute.

Consulting Interests:

As a Data Scientist, my main consulting interest is in the Analytics sector to provide analytical solutions across industry verticals. I work across multiple functions in consulting, including advanced statistical modeling in different analytical domains using both structured and unstructured information with global organizations. I have established and headed an offshore credit risk modeling team and managed onsite projects in the UK and USA for premier banking and financial institutions.

Experience

Work Experience:

Assistant Professor, Indian Institute of Management, Calcutta, 2022 - Present
Lecturer in Mathematical Data Science, Queen Mary University of London, 2021 - 2022
Visiting Researcher, The Alan Turing Institute, 2018-2022
Research Scientist, Imperial College London, 2018 - 2021
National Postdoctoral Fellow, Indian Institute of Engineering Science and Technology (IIEST), Shibpur, 2017 - 2018
Consultant Statistician, Freelance, 2009 - 2017
Consultant, Price Waterhouse Coopers, 2007 - 2009
Risk Analyst, Genpact, 2006 - 2007
Statistical Analyst, Marketics, 2006

Research

Journal Publications:

• Bhuyan P*, Chatterjee K*, 2024, Estimating prevalence of post-war health disorders using multiple systems data, Scientific Reports, 14, 14763. 
• Bhuyan P*, Chatterjee K*, 2024, Estimation of population size with heterogeneous catchability and behavioural dependence: applications to air and water borne disease surveillance, Journal of the Royal Statistical Society: Series A (Statistics in Society), 187(1), Pages: 110–131. 
• Bhuyan P*, Jana K*, McCoy E, 2023, Causal Analysis at extreme quantiles with application to London traffic flow data, Journal of the Royal Statistical Society: Series C (Applied Statistics), 72(5), Pages: 1452–1474.
• Ghosh S, Bhuyan P, Finkelstein M, 2022, On a bivariate copula for modeling negative dependence, Statistical Methods and Applications, Vol: 31, Pages: 1329–1353. 
• Bhuyan P, McCoy E, Li H, Graham D J, 2021, Analysing causal effects of the London cycle superhighways on traffic congestion, Annals of Applied Statistics, Vol: 15 (4), Pages: 1999-2022. 
• Jha J*, Bhuyan P*, 2021, Two-stage circular-circular regression with zero-inflation: application to medical sciences, Annals of Applied Statistics, Vol: 15 (3), Pages: 1343-1365. 
• Nanda P, Bhuyan P, Dewanji A, 2021, Optimal replacement policy under cumulative damage model and strength degradation with applications, Annals of Operations Research, Vol: 315, Pages: 1345–1371. 
• Bhuyan P, Ghosh S, Majumder P, Mitra M, 2020, A bivariate life distribution and notions of negative dependence, Stat, Vol: 9(1). 
• Jha P, Banerjee S, Bhuyan P, Sudarshan M, Dewanji A, 2020, Elemental distribution in urban sediments of small waterbodies and its implications: a case study from Kolkata, India, Environmental Geochemistry and Health, Vol: 42, Pages: 461-482. 
• Chattopadhyay N, Bhuyan P, 2020, Player selection strategy: a quantitative perspective, Proceedings of the 62nd ISI World Statistics Congresses: Contributed Paper Session, Vol-3.
• Bhuyan P, 2019, Estimation of random-effects model for longitudinal data with nonignorable missingness using Gibbs sampling, Computational Statistics, Vol: 34, Pages: 1693-1710. 
• Bhuyan P, Biswas J, Ghosh P, Das K, 2019, A Bayesian two-stage regression approach of analysing longitudinal outcomes with endogeneity and incompleteness, Statistical Modelling, Vol: 19, Pages: 157-173. 
• Chatterjee K, Bhuyan P, 2019, On the estimation of population size from a post-stratified two-sample capture-recapture data under dependence, Journal of Statistical Computation and Simulation, Vol: 90, Pages: 819-838. 
• 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), Vol: 182, Pages: 1487-1501. 
• Bhuyan P, Mitra M, Dewanji A, 2018, Identifiability issues in dynamic stress-strength modeling, Annals of the Institute of Statistical Mathematics, Vol: 70, Pages: 63-81. 
• Bhuyan P, Sengupta D, 2017, Estimation of reliability with semi-parametric modeling of degradation, Computational Statistics & Data Analysis, Vol: 115, Pages: 172-185, ISSN: 0167-9473. 
• Bhuyan P, Dewanji A, 2017, Estimation of reliability with cumulative stress and strength degradation, Statistics, Vol: 51, Pages: 766-781. 
• Bhuyan P, Dewanji A, 2017, Reliability computation under dynamic stress-strength modeling with cumulative stress and strength degradation, Communications in Statistics - Simulation and Computation, Vol: 46, Pages: 2701-2713. 
  
  * contributed equally.

Conferences:

• RSS International Conference, Brighton, September, 2024
• ASC & OZCOTS, Wollongong, December 2023
• 2022 International Indian Statistical Association Conference, Bangalore, December, 2022
• RSS International Conference, Manchester, September, 2021
• 2019 International Indian Statistical Association Conference, Mumbai, December, 2019
• European Meeting of Statisticians, Palermo, July, 2019
• 11th International Conference on Mathematical Methods in Reliability, Hong Kong, June, 2019
• CFE-CMStatistics 2018, Pisa, December, 2018
• ISBA World Meeting, Edinburgh, June, 2018
• 2017 International Indian Statistical Association Conference, Hyderabad, December, 2017
• 22nd International Conference on Computational Statistics,  Oviedo, August, 2016
• 2015 International Indian Statistical Association Conference, Pune, December, 2015
• Eighth International Workshop on Simulation, Vienna, September, 2015
• 22nd International Conference on Computational Statistics, Oviedo, August, 2016
• Eighth International Workshop on Simulation, Vienna, September, 2015
• 21st International Conference on Computational Statistics, Geneva, August, 2014

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.