I'm a lecturer in applied mathematics at the University of Adelaide. My research interests are in computational social science, human dynamics and social networks, data assimilation, and the mathematics of weather and climate. Please see my website, http://maths.adelaide.edu.au/lewis.mitchell/, for further details.
This project combines large open datasets from social media with predictive machine learning models to predict social unrest events and monitor trends relevant to elections, both in Australia and abroad. The work is done in collaboration with researchers and software engineers from Data to Decisions CRC.
According to a theory known as the ‘friendship paradox’, the answer to that question is: not as popular as your friends. This paradox basically states that friends of individuals tend to have more friends than the individual him/herself.
Modelling how information flows over social networks is fundamental to understanding important phenomena such as memes, vitality, and social influence. This project combines new mathematical techniques for estimating social information flow and predictability with large-scale data science methods to better understand the information dynamics within social networks.
Prizes, awards and special recognition
ECMS Faculty Learning and Teaching Award was awarded to Lewis Mitchell. Awarded from the University of Adelaide.
Mitchell, L., & Carrassi A.
(2015). Accounting for model error due to unresolved scales within ensemble Kalman filtering. Quarterly Journal of the Royal Meteorological Society. 141(689), 1417-1428. doi: 10.1002/qj.2451