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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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of cell death and discovering small molecules that target mitochondrial adaptations to overcome therapeutic resistance in cancer. We integrate approaches from chemical biology, mitochondrial biology, cancer
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Sequencing (GBS), Whole Genome Sequencing (WGS) and targeted genotyping chips (e.g. MassArray) and analyze them together with environmental data using population genetics and machine learning tools
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