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. The titles of the posters are "3D Deep Learning in High Resolution Point Clouds" and "Nonsymmetric geometry-oblivious fast multipole method".
Technical University of Munich
Department of Informatics
Office: MI 02.05.040
Mail: ivana.jovanovic (at) tum.de
Office Hours: by arrangement
- Doctoral candidate, TUM Graduate School - CeDoSIA
- Research Associate (Wissenschaftlicher Mitarbeiter) at TUM SCCS since June 2018
- M.Sc. with Honours in Computational Science and Engineering, Technical University of Munich, 2018
- Diploma in Electrical Engineering, University of Belgrade, Signals and Systems Department, 2014
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 distribured hydrological models)
- Sparse Grids Methods
- Inverse problems - Bayesian Inference
- Machine Learning
Best Poster Award at CoSaS 2018
If you are interested in a student project (Bachelor's or Master's Thesis or anything else), please contact me directly. See also the list of Student Projects at our chair.
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.
Open student projects
Running student projects
- Jonas Fill: Development of the Bayesian Recurrent Neural Network Architectures for Hydrological Time Series Forecasting, Bachelor's Thesis, Fakultät für Informatik, Informatics, since Mar 2021
- Hanna Weigold: Second Order Methods for Bayesian Neural Networks, Master's Thesis, Mathematics in Science and Engineering, since December 2020. Joint supervision with Severin Reiz
- Second-Order Optimization Methods for Bayesian Neural Networks. Masterarbeit, 2021 mehr… BibTeX
- Development of the Bayesian Recurrent Neural Network Architectures for Hydrological Time Series Forecasting. Bachelorarbeit, 2021 mehr… BibTeX Volltext (mediaTUM)
- Development of Recurrent Neural Network Architectures for Hydrological Time Series Forecasting. Bachelorarbeit, 2021 mehr… BibTeX Volltext (mediaTUM)
- Parallel Evaluation of Adaptive Sparse Grids with Application to Uncertainty Quantification of Hydrology Simulations. Projektarbeit, 2020 mehr… BibTeX Volltext (mediaTUM)
- Developing a prototype of Bayesian Inference framework to recalibrate the complex hydrological model LARSIM. Studienarbeit, 2020 mehr… BibTeX Volltext (mediaTUM)
Winter semester 2020/21
Summer semester 2020
- Seminar Data Mining (IN0014, IN4927)
- Seminar High Dimensional Methods in Scientific Computing (IN2107, IN0014, IN218306)
Winter semester 2019/20
- Scientific Computing 1 (IN2005) (Moodle)
- Seminar Computational Aspects of Machine Learning (IN2107,IN0014, IN2183)
Summer semester 2018/19
Winter semester 2018/19
- Scientific Computing 1 tutorials (Moodle)
- Assisting in the seminar Next Generation High-Performance Computing.
Winter semester 2016/17
- Assisting in the Scientific Computing 1 tutorials