Duncan Watson-Parris, PhD, is an atmospheric physicist working at the interface of climate research and machine learning. The Climate Analytics Lab he leads focuses 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 that make modeling average responses to any perturbation in aerosol extremely challenging. The lab is leading the development of a variety of machine-learning tools and techniques to alleviate these difficulties and, optimally, combine a variety of observational datasets – including global satellite and aircraft measurements – to constrain and improve these models.

Duncan has won both an NSF CAREER award and a Google Academic Research award. He is also keen to foster the application of machine learning to climate-science questions more broadly and convenes the "Machine Learning for Climate Science" EGU session and co-convenes the "AI and Climate Science" discovery series that is part of the United Nations' AI for Good program.