Background

• 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

Research interests

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

Open and running student projects

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

see: https://www.in.tum.de/i05/jobangebote-studentische-projektarbeiten/job-offers-student-projects/uncertainty-quantification/

You can also come to my office and discuss possible topics.

Running student project