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Last modified: April 2019


Chaitanya Joshi

Chaitanya Joshi
Ph.D (2011 - TCD), M.Sc. (2003 - IIT-K), B.Sc. (2001 - Mumbai)

Senior Lecturer,
Department of Mathematics and Statistics,
Univerisity of Waikato, Hamilton, New Zealand.
email: cjoshi@waikato.ac.nz

Current Research Interests: Computational Bayesian Inference, Bayesian Robustness, Statistical Modelling.


My Research: I am interested in both: (i) Theoretical and methodological research in Bayesian methods as well as (ii) in modeling complex real life processes using statistical methods.

Theoretical and methodological research: I am interested in developing computationally efficient methods for Bayesian inference. Along with Stephen Joe and our Ph.D. student Paul Brown, we have developed a computationally efficient algorithm based on low discrepancy sequences which can be used in grid-based Bayesian methods such as INLA. I am also currently working on Approximate Bayesian Computation (ABC) methods.

Recently, I have also started working on problems in Bayesian robustness - specifically on prior robustness. I am interested in developing prior robustness methods for Bayesian models using the distorted band class of priors.

Statistical Modelling: I am part of the newly formed NZ Institute of Secutiry and Crime Science. I am working on modelling crime in collaboration with NZ Police. Recently, I have started working on the Adversarial Risk Analysis (ARA) in collaboration with David Rios (ICMAT) and others.

From time to time, I collaborate with scientists on interesting modelling problems. This includes some recent work with Louis Schipper's group on incorporating uncertainty using Bayesian methods. Over the last few years, I also worked with Daniel Laughlin (now at Univ. of Wyoming, U.S.) on problems related to modeling species distribution. We developed a novel mathematical framework called 'Traitspace' which incorporates the various processes/factors which govern the assembly of ecological communities via their functional traits and predicts the community assembly by using the observed trait values.

Previous statistical work/interests include: Efficient Bayesian inference on Stochastic Differential Equation (SDE) models, Epidemiology, Clinical trials, Demand estimation and Market research.

From 2003 until 2007, I worked as a statistician for a number of leading corporations in the pharmaceutical and market research area.

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PUBLICATIONS and selected CONFERENCE presentations:

2019

2018

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2012

2011

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2007


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COURSES I teach/have taught: