Research interests

  • Machine Learning, Uncertainty Quantification and Big Data
    • multi-fidelity machine learning (e.g. by sparse grid combination technique)
    • approximate training (low-rank approximation by e.g. hierarchical matrices)
    • stochastic collocation, Bayesian inference / data assimilation
    • basic research wrt. reproducing kernel Hilbert spaces / Gaussian processes
  • High Performance Computing
    • numerics / algorithms for many-core processors (e.g. GPUs)
    • scalable distributed-memory parallel computing in machine learning and scientific computing
  • Interdisciplinary applications
    • material science, quantum chemistry (data from DFT, CC, etc.)
    • paleo-climate reconstruction (calibration, etc.)
    • fluid mechanics (two-phase flows, plasma physics)
    • medical imaging (dynamic contrast-enhanced imaging)



  • Machine learning