Dr Ilja Klebanov

scientific researcher

Zuse Institut Berlin
Takustraße 7
14195 Berlin
+49 (0) 30 03084185321
Takustraße 7

Research focus

Bayesian Inference, Empirical Bayes methods, Data Analysis, Uncertainty Quantification

Projects as a member

  • CH6

    Uncertainty quantification for Bayesian inverse problems with applications to systems biology

    Prof. Dr. Susanna Röblitz / Prof. Dr. Christof Schütte

    Project heads: Prof. Dr. Susanna Röblitz / Prof. Dr. Christof Schütte
    Project members: Dr Ilja Klebanov
    Duration: -
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin


    In biotechnology, systems biology, or reaction engineering one is faced with large systems of ordinary differential equations (ODE) that are used to describe the kinetics of the reaction network of interest. These ODE models contain a large number of mostly unknown kinetic parameters that one needs to infer from usually sparse and noisy experimental data. Typically, inverse problems like classical parameter identification are associated with ill-posed behaviour. However, Bayesian approaches can be used to recover joint parameter distributions and allow for the quantification of uncertainty and risk in a way demanded by the applications. In this project, we want to overcome the computational limitations of classical Markov-chain Monte-Carlo methods by developing new algorithmic approaches to Bayesian inverse problems using, e.g., sparse approximation results or empirical Bayes methods. The methods will directly be applied to large-scale networks in systems biology.


Projects as a guest