Sam Clifford

I’m a statistician working at Queensland University of Technology on a range of environmental and environmental health problems such as jaguar conservation, reef conservation, and air pollution and its health impacts. In addition, I teach mathematics and statistics to first year science students in the unit SEB113 - Quantitative Methods in Science.

Methodologically, my research interests are

  • Generalised Additive Models
  • Bayesian spline regression
  • Spatial and spatio-temporal statistics
  • Bayesian hierarchical linear models
  • Exposure-response models

A list of key publications is available here, and a full list at Google Scholar and ORCID.


My undergrad was in applied and computational mathematics, focussing on fluid dynamics in my honours year under the supervision of Dr Glenn Fulford and Dr Tim Moroney.

Between 2009 and 2013 I was a PhD student at the International Laboratory for Air Quality and Health under the supervision of Professors Lidia Morawska and Kerrie Mengersen and Dr Sama Low Choy, where I completed a thesis on spatio-temporal modelling of air pollution.

Postdoctoral employment in Mathematics and Statistics

From 2016 I have been employed as a Postdoctoral Fellow at the QUT node of the ARC Centre of Excellence for Mathematical and Statistical Frontiers, working on a variety of environmental statistics problems such as coral cover in the Great Barrier Reef and jaguar conservation in Peru as well as the dynamics of dengue fever.

Biostatistics for virus dynamics

I am currently working with colleagues at QUT and QIMR to investigate the replication and spread of Dengue fever. The work involves mathematical and statistical modelling of the dynamics of the virus cell, the host and transmission within the population.

Non-parametric modelling of time series data

I am currently working on simulation studies for two papers with a former ACEMS PhD student, Dr Zoe van Havre, where we are looking at finite and infinite mixture modelling methods for classifying action potential data from EEG scans. Early work in my PhD focussed on modelling temporal variation without spatial structure.

Citizen science for spatial statistics

Work at ACEMS in 2016-2017 focussed on incorporating citizen scientists into traditional spatio-temporal modelling such as species distribution modelling of jaguars and coral cover.

The Monitoring Through Many Eyes is a collaboration between scientists, data analysts and marine explorers, working together to document, analyse and predict the health of the Great Barrier Reef. The aim is to tap into the power of Citizen Scientists by collating thousands of underwater images take by recreational divers and snorkelers on the Reef each year.

Through our Many Eyes on the Wild, we aim to facilitate faster, better decisions about management and monitoring. We are developing and using these approaches to help conserve jaguars in the Peruvian Amazon. We are addressing this problem by combining traditional conservation with virtual reality technology, mathematical and statistical modelling, local knowledge and international expertise.

Postdoctoral employment in Aerosol Science

Between 2013 and 2015 I was employed as a Postdoctoral Fellow at ILAQH to support the research being done across a variety of topics in air quality. The bulk of my postdoctoral work at ILAQH was related to the Ultrafine Particles from Traffic Emissions and Children’s Health project. The project seeks to determine the effect of the exposure to airborne nano and ultrafine (UF) particles emitted from motor vehicles on the health of children in schools.

Research from my PhD and postdoctoral time with ILAQH has resulted in a number of papers looking at spatio-temporal modelling of urban air pollution with a view to estimating human exposure and modelling its health impact.


Occasionally I publish useful code, including a package for tidying MCMC output from coda or rjags entitled mmcc, and a package to bring some of the common linear model diagnostics like variance inflation factors and tables of confidence intervals to GAMs from mgcv in mgcv.helper.


Since 2013 I have been involved in the development and delivery of SEB113 - Quantitative Methods for Science as part of the ST01 Bachelor of Science course at Queensland University of Technology. The course covers a variety of mathematical and statistical topics taught through scientific case studies and makes use of the R language for all computation.

In 2015, the teaching team was the recipient of the Vice Chancellor’s Performance Award for innovation in redesiging the unit for student success through encouraging engagement with multiple technologies.