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Amos Storkey is a Professor in the School of Informatics at the University of Edinburgh with a longstanding history in researching machine learning methods. His work involves generative models, generative AI, and deep learning methodologies, focusing on improving understanding and efficiency of models. He supervises PhD students and postdocs, contributing significantly to areas such as understanding deep learning, building neural networks under real-world constraints, meta-learning, and exploration-driven reinforcement learning. Storkey's expertise also includes Bayesian methods, Gaussian processes, and graphical models. His current research spans stochastic differential systems, sampling Bayesian posteriors, and machine learning markets. He has a keen interest in medical imaging, particularly in brain and retinal imaging, as well as in the application of learning to generate music. His projects and publications can be explored further on the Bayeswatch group website.
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