MEET THE LECTURER: DR CHRIS DROVANDI
Queensland University of Technology
Chris Drovandi’s research interests lie in developing new methods and algorithms in Computational Bayesian Statistics, and Bayesian optimal experimental design, as well as the translation of Bayesian methods to problems across many different disciplines such as biology, ecology, medicine, exercise science and finance.
Chris completed his PhD in 2012, and received a University Vice Chancellor’s Performance Award for Research in 2016. He is currently a Senior Lecturer in Statistics at the Queensland University of Technology, Brisbane and is the recipient of the Australian Research Council’s Discovery Early Career Researcher Award (3 years), which started in 2016.
Chris has extensive experience in teaching many statistical topics such as probability, stochastic modelling, regression and generalised linear models, Monte Carlo methods and Bayesian statistics. In 2015, he was invited to deliver a course on Computational Bayesian Statistics at the AMSI Summer School in Newcastle.
Dr Chris Drovandi will be lecturing at AMSI Winter School 2017, delivering a course on “Introduction to Bayesian Computational Methods via Markov Chain Monte Carlo Algorithms”.
1. Can you tell us about your work? What drives your interest in this field?My main research interest is in developing new Bayesian computational methods. I love researching in this field as I can be at the forefront of expanding the class of Bayesian statistical models that can be considered by practitioners. I enjoy thinking about how tasks can do done more efficiently and making use of my programming skills.
2. What do you consider your biggest achievement to date?
My greatest academic achievement to date was successfully obtaining an Australian Research Council Grant (Discovery Early Career Research Award, approximately 15% acceptance rate). However, my proudest achievement was when my first PhD student (as the principal supervisor) completed her degree last year.
3. Why did you become a mathematician/statistician?
As my mother recalls, I have always been fascinated by numbers – from counting ants as a toddler to learning how to use my toy abacus. What attracted me to Statistics however is the sheer diversity it offers in research methods and applications. Being such a dynamic field, there is always something new to read about and explore. Statistics has exposed me to a wide range of areas, such as modelling, simulation, computation, inference, theory, data analysis, visualisation and machine learning.
4. Do you have any advice for future researchers?
Patience, persistence, perfection and creativity.