Arnab Hazra
Indian Institute of Technology Kanpur, King Abdullah University of Science and Technology (KAUST), North Carolina State University, Indian Statistical Institute
0000-0002-3647-2697
About
Currently, I am an Assistant Professor of Statistics at the Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur, India. I obtained my Ph.D. in Statistics in 2018 from the Department of Statistics, North Carolina State University, Raleigh, United States, under the supervision of Brian J. Reich and Ana-Maria Staicu. Before that, I completed my Bachelors of Statistics and Master of Statistics from the Indian Statistical Institute, Kolkata, in 2012 and 2014, respectively. After my Ph.D., I was a postdoctoral fellow of Statistics at the Computer, Electrical, and Mathematical Science and Engineering (CEMSE) Division, at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Employment
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Indian Institute of Technology Kanpur Assistant Professor2021 - Present
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King Abdullah University of Science and Technology (KAUST) Post-Doctoral Fellow2020 - 2021
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King Abdullah University of Science and Technology Postdoctoral Fellow of Statistics2019 - 2021
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King Abdullah University of Science and Technology Remote Consultant2018 - 2019
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Indian Statistical Institute Visiting Scientist2018 - 2018
Education
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North Carolina State University PhD2014 - 2018
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Indian Statistical Institute Master of Statistics2012 - 2014
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Indian Statistical Institute Bachelor of Statistics2009 - 2012
Projects & Funding
Projects & funding information is unavailable.
Publications (16)
- Scalable Bayesian inference for high-dimensional mixed-type multivariate spatial data Save
- Reliability of a System Subjected to a Cumulative Shock Model With a Change Point Save
- A Bayesian latent Gaussian conditional autoregressive copula model for analyzing spatially-varying trends in rainfall Save
- A semiparametric generalized exponential regression model with a principled distance-based prior Save
- Approximate Bayesian Inference for High-Resolution Spatial Disaggregation Using Alternative Data Sources Save
- A utopic adventure in the modelling of conditional univariate and multivariate extremes Save
- Estimating changepoints in extremal dependence, applied to aviation stock prices during COVID-19 pandemic Save
- Robust Statistical Modeling of Monthly Rainfall: The Minimum Density Power Divergence Approach Save
- Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study Save
- Efficient Modeling of Spatial Extremes over Large Geographical Domains Save