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Identification of dynamical subpopulations, optimal experiment design and more

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If you have a question about this talk, please contact Karsten Borgwardt.

In the talk I present some current work and introduce my research interests. These can be summarized as machine learning approaches for the reduction of uncertainty in dynamical systems, especially in systems biology.

First, I describe a method that, given scarce experimental observations of the dynamics of a cell population, estimates the presence of heterogeneously parametrized cell subpopulations.

Then, I present an approach to design optimal experiments for the mentioned identification of dynamical subpopulations.

Finally, I propose a framework, based on observability theory, to identify models for dynamical systems, given noisy measurements and domain knowledge expressed as functional prior information.

This talk is part of the Machine Learning @ CUED series.

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