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Michael U. Gutmann is a Senior Lecturer in the School of Informatics at the University of Edinburgh. He specializes in the field of Machine Learning, particularly in developing Bayesian inference methods. His research focuses on the science of machine learning, with an emphasis on creating innovative solutions for inference design. His contributions include work on vector copulas for mutual information estimation and simulations for Bayesian inference addressing model misspecification. He has published several papers on topics such as risk-averse optimization in genetic circuits and enhancing variational autoencoders with incomplete data. Through his research, he aims to advance the understanding and application of machine learning techniques in various domains.
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