I am an EPSRC Innovation Fellow and principal investigator for a project concerning automatic disease detection and monitoring in calves. The project uses artificial intelligence techniques, coupled with visible-range and thermal cameras, to identify Bovine respiratory disease (BRD), the most common and costly disease affecting cattle in the world, at the earliest possible stage. I was previously a Senior Research Associate looking after the "Camouflage Machine" project, which combined machine learning, visual psychophysics and computer graphics to either optimise camouflage or maximise visibility for a range environments. As well as visual perception, my research interests lie in the areas of representation of uncertainty and behavioural decision making. I have also carried out work in the areas of locomotion, experimental aesthetics, colour and eye movements.
I was awarded my PhD in 2012, which I studied after graduating with a joint honours degree in Psychology and Philosophy, also at Bristol. My third year project, supervised by Prof. Tom Troscianko, investigated natural aesthetics, subjective judgements and eye movements. Following my PhD I have been a post doctoral researcher in the School of Experimental Psychology, except for a short time working for AIG, a large insurance company, on a project integrating scientific psychological principles into business improvement: This was a valuable experience because it involved working with Daniel Kahneman, Nobel Laureate and father of Behavioural Economics, and Max Bazerman, Harvard professor and leading authority on negotiation. Prior to studying at Bristol I spent more than 20 years in the software industry in positions from programmer to Technical Director.
Fennell, J. G., Talas, L., Baddeley, R. J., Cuthill, I. C., Scott-Samuel, N. E. 2020. The Camouflage Machine: Optimising protective colouration using deep learning with genetic algorithms. bioRxiv, 903484.
Talas, L., Fennell, J. G., Kjernsmo, K., Cuthill, I. C., Scott-Samuel, N. E., Baddeley, R. J. In Press. CamoGAN: Evolving optimum camouflage with Generative Adversarial Networks. Methods in Ecology and Evolution.
Fennell, J. G., Talas, L., Baddeley, R. J., Cuthill, I. C., Scott-Samuel, N. E. Optimising colour for camouflage and visibility using deep learning: the effects of the environment and the observer’s visual system. Journal of the Royal Society Interface, 16, 20190183.