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
Machine Learning
Open and running student projects
Runnin student projects
Boris Liu: "Accounting for parameter uncertainty and time-varying sensitivity analysis of the HBV-SASK hydrologic model using sequential data assimilation and pseudo-spectral methods". Bachelor's thesis, CIT School - Computer Science Department; Since October 2023Hi Tobi,
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.
Boris Liu: Accounting for states and parameter uncertainty of the HBV-SASK hydrologic model using particle filtering as a sequential data assimilation technique. Bachelor thesis, 2024 more…BibTeX
Markus Englberger: Using the Spatially Adaptive Combination Technique for Efficient Quantification of Uncertainty in Hydrological Models. Bachelor thesis, 2022 more…BibTeX
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2021
Hanna Weigold: Second-Order Optimization Methods for Bayesian Neural Networks. Master thesis, 2021 more…BibTeX
Jonas Fill: Development of the Bayesian Recurrent Neural Network Architectures for Hydrological Time Series Forecasting. Bachelor thesis, 2021 more…BibTeX
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Simon Zocholl: Development of Recurrent Neural Network Architectures for Hydrological Time Series Forecasting. Bachelor thesis, 2021 more…BibTeX
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2020
Jonas Treplin: Parallel Evaluation of Adaptive Sparse Grids with Application to Uncertainty Quantification of Hydrology Simulations. Projekt thesis, 2020 more…BibTeX
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Mathieu Putz: Developing a prototype of Bayesian Inference framework to recalibrate the complex hydrological model LARSIM. Studien thesis, 2020 more…BibTeX
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2019
Frank Schraufstetter: Development of a Prototype to Quantify the Uncertainty of the Water Balance Model LARSIM. Bachelor thesis, 2019 more…BibTeX
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Vyshakh Unnikrishnan: Implementation of a deep learning based model for rainfall-runoff modelling. Master thesis, 2019 more…BibTeX
Ivana Jovanovic and Severin Reiz scored the 1st and 2nd place in the Best Poster Jury Award at CoSaS meeting in Erlangen, Germany, among 70 posters.…
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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…
2021
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…
Other activities
Organizational support for "BGCE Student Paper Prize" for the best paper at the SIAM CSE (2019, 2021)