Microscopic Image Analysis

We are broadly interested in Bayesian inference and its application to scientific data. We view probability theory as the mathematical framework for making consistent inferences and predictions from incomplete and uncertain information. Therefore, Bayesian methods are powerful tools to support scientific inference and to solve engineering problems. Our past and current research focuses on Bayesian computation (in particular Markov chain Monte Carlo) and applications in structural biology (protein structure determination, cryo-electron microscopy).
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