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Postdoctoral Fellow with Professor Samuel Kou. Professor Kou’s group focuses on research in statistical modeling and stochastic inference in protein folding, biology, chemistry and medicine, Bayesian inference
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Machine Learning Seminar Group Advanced Tutorial Lecture Series on Machine Learning Non-Parametric Bayes Tutorial Course (October 9, 16 and 28, 2008) Bayesian statistics in other labs Machine Learning and
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, geospatial statistics, Bayesian statistics, burden mapping, measuring the impact of the environment on disease among others. The PI has projects in both infectious and chronic disease, measuring the impact of
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health data; knowledge of infectious disease transmission dynamics; and competence in applied Bayesian statistical modelling. Strong quantitative background, with demonstrated ability to program in one
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high information content Flow MRI datasets with physics based modelling and Bayesian inference to determine constitutive models for non-Newtonian and other complex fluids in situ. The project will
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Ability to analyse and visualise disease surveillance and population health data; knowledge of infectious disease transmission dynamics; and competence in applied Bayesian statistical modelling. Strong
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the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts of complex models. BioM is an
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied