Dr Sam Clifford
B AppSc PhD
in Machine (Statistical) Learning
Sam obtained a Bachelors Degree in Applied Science (with Honours) from Queensland University of Technology (QUT, Brisbane, Australia), majoring in Applied and Computational Mathematics. After a short break teaching mathematics to video game developers at Krome Studios, Sam undertook his Doctoral studies at the International Laboratory for Air Quality and Health (ILAQH), a WHO Collaborating Centre, at QUT.
Under Professors Lidia Morawska and Kerrie Mengersen and Dr Samantha Low-Choy, Sam's thesis investigated spatio-temporal modelling of ultrafine particles in Brisbane, Australia. After five and a half years of postdoctoral work at QUT (ILAQH and the ARC Centre of Excellence for Mathematical and Statistical Frontiers), Sam joined the London School of Hygiene and Tropical Medicine for 2.5 years as a postdoctoral fellow.
In 2020 Sam was promoted to Assistant Professor, where he continues his work on streptococcus pneumoniae, SARS-CoV-2/COVID-19 and Dengue.
Sam's current teaching at LSHTM includes the following short courses:
- Modern Techniques for Modelling Infectious Disease Dynamics (Module Organiser)
- Introduction to Spatial Analysis in R (Teaching staff)
and the following MSc modules:
- 2490 - Machine Learning (lectures on prediction)
- 2491 - Data Challenge (Module Organiser)
- 2021 - Statistics for Epidemiology and Population Health (practicals)
- 2031 - Introduction to Statistical Computing (guest lectures on visualisation and data wrangling)
Sam is currently enrolled in the Postgraduate Certificate in Learning and Teaching at LSHTM.
Prior to joining LSHTM, Sam lectured mathematics and statistics to first year Bachelor of Science students at QUT in the core unit SEB113 - Quantitative Methods for Science. His teaching makes use of blended learning approaches to prepare students for lecture material, and collaborative workshops with project-based learning.
In 2015, Sam and his team were awarded a Vice Chancellor's Award for Innovation on the basis of their work transforming SEB113.
Sam's research at LSHTM includes the following:
- Traveller screening, contact tracing and quarantine for SARS-CoV-2/COVID-19
- Spatio-temporal variation in Streptococcus pneumoniae serotypes
- Maternal colonisation of Group B Streptococcus
- Modelling the health and economic impact of dengue vaccination
In addition to his doctoral thesis on spatio-temporal modelling of ultrafine particle number concentration, Sam's research interests include
- exposure assessment
- personal monitoring equipment for air pollution
- non-parametric regression
- jaguar conservation
- Great Barrier Reef conservation
- machine learning for spatial data
- Bayesian hierarchical modelling
- data visualisation
- mathematics and statistics education