Duncan Watson-Parris is an atmospheric physicist working at the interface of climate research and machine learning. Currently his research focusses on understanding the interactions between aerosols and clouds, and their representation within global climate models. These interactions are numerous and complex, involving non-linearities and feedbacks which make modelling average responses to any perturbation in aerosol extremely challenging. Duncan has recently led the development of a variety of machine learning (ML) tools and techniques to alleviate these difficulties and optimally combine a variety of observational datasets, including global satellite and aircraft measurements, to constrain and evaluate these models. Duncan also works to foster the application of machine learning to climate science questions more broadly and is Course Director of the iMIRACLI Innovative Training Network, convenes the Machine Learning in Climate research forum within the University of Oxford and co-chaired the recent Climate Informatics 2020 conference.