My research focuses on applied and computational mathematics, particularly on uncertainty quantification (UQ), modeling and system identification, inverse problems, and data-driven model learning. The main applications driving my research are from hydrology. More precisely, in my work, I am bridging the gap between theoretical work on High-dimensional Uncertainty Quantification and Bayesian Inversion, applied to relatively simple simulation models, and more complex real-world problems.
High-dimensional Forward Uncertainty Quantification and Sensitivity Analysis (mainly, analysis of conceptual distributed hydrologic models)
Sparse Grids Methods
Inverse problems - Bayesian Inference
Open and running student projects
Open student projects
If you are interested in a student project (Bachelor's or Master's Thesis or anything else), it is the best to contact me directly. Here is the list of some projects that I would offer at the moment (the list is not exhaustive):
"UQ and SA of Hydrologic model HBV using pyApprox software tool"
Do you want to know what other students are working on in our chair? You are warmly encouraged to attend their presentations at the SCCS Colloquium! Come to get ideas, meet your potential supervisor, or to learn from the style of others for your own presentation.
Ivana Jovanovic Buha; Michael Obersteiner; Tobias Neckel; Hans-Joachim Bungartz: Efficient Uncertainty Quantification and Global Time-Varying Sensitivity Analysis Using the Spatially Adaptive Combination Technique. SIAM Conference on Uncertainty Quantification (UQ22), SIAM, 2022Atlanta, Georgiamore…
Ivana Jovanovic Buha; Florian Künzner; Tobias Neckel; Hans-Joachim Bungartz: Efficient Uncertainty Quantification and Global Time-Varying Sensitivity Analysis of Conceptual Hydrological Model. SIAM Conference on Computational Science and Engineering (CSE21), SIAM, 2021Fort Worth, Texas, U.S.A.more…
Organizational support for "BGCE Student Paper Prize" for the best paper at the SIAM CSE (2019, 2021)