Suzy Moat is Professor of Behavioural Science at Warwick Business School, where she co-directs the Data Science Lab. She is also a Fellow of The Alan Turing Institute. Her research investigates whether data on our usage of the Internet, from sources such as Google, Wikipedia and Flickr, can help us measure and even predict human behaviour in the real world.
Moat's work touches on problems as diverse as linking online behaviour to stock market moves (with Preis, Curme, Stanley, et al.), estimating crowd sizes (with Botta and Preis) and evaluating whether the beauty of the environment we live in might affect our health (with Seresinhe and Preis). The results of her research have been featured by television, radio and press worldwide, by outlets such as CNN, BBC, The Guardian, Wall Street Journal, New Scientist and Wired.
Moat studied Computer Science at UCL and Psychology at the University of Edinburgh and won a series of prizes during her studies. With her collaborator and Data Science Lab co-director Tobias Preis, she led an online course on using big data to measure and predict human behaviour which has attracted over 25,000 learners to date. Moat has also acted as an advisor to government and public bodies on the predictive capabilities of big data.
Selected media coverage of her work includes:
"Which countries are the most forward thinking? See it visualised"
"Online searches for future linked to economic success"
"Google searches predict market moves"
"Wikipedia page views could predict stock market changes"
"Do politics-themed Google searches predict stock activity?"
"Crowds 'could be counted' with phone and Twitter data
You can follow Suzy on Twitter at @suzymoat and see her publications on Google Scholar.
Research InterestsWe increasingly rely on networked computer systems and smart cards to support our everyday activities, and everything we do generates data - whether buying bread at the supermarket, taking a ride on the Tube, or calling a friend for a chat.
This data is opening up a new era for our understanding of human behaviour - and also for policy making and business processes which depend upon this understanding.
As a computational social scientist, my research investigates how the vast amounts of data generated by our everyday use of technology can help us understand and even predict how humans behave.
In June 2014, I chaired Europe's first Computational Social Science Conference with Mark Carrigan and Tobias Preis.
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