My research interests are in the field of mathematical methods for computational science, ranging from numerical methods to optimization, uncertainty quantification, and scientific machine learning. I am committed to reproducibility in computational science, facilitated by open science. Through outreach projects, I have become interested in mathematics education, specifically teaching modern applied mathematics in high-schools. 

At SCC, I manage the high-performance computing systems, with a specific focus on energy efficiency.

The Scientific Computing & Mathematics department links the activities of three divisions. In the interdisciplinary research group Computational Science and Mathematical Methods, everything revolves around modelling and numerical methods research. It brings together researchers from the SCC, the Institute of Applied and Computational Mathematics and external partners from industry and science. Experts from our Simulation and Data Life Cycle Labs support researchers who perform calculations on our supercomputers. They help with the implementation of their simulation models and organize training courses to facilitate their entry into high-performance computing. And for the scientists of tomorrow, we offer workshops and project days with CAMMP and Simulated Worlds. Interested teachers can bring our experts directly to their school and solve exciting, real-world problems with the help of mathematical modeling.

As team leader, I support the PhD-students and students assistants of the CAMMP team in the organization and implementation of CAMMP activities and the associated educational research. I prepared myself for this position through my doctorate in mathematics education on the teaching and learning of machine learning methods for engineering students.

My own research projects deal with educational research on the accessibility of complex mathematical content and the effectiveness of different teaching-learning formats. In this context, I collaborate with researchers from other universities and institutions such as the AI-Campus.

As a PhD student of the CAMMP at KIT, I am part of the Computational Science and Mathematical Methods (CSMM) group at KIT and part of the Simulierte Welten project. My research interests are in the field of mathematics education, data literacy, and data cleaning. As part of my doctoral thesis, I develop teaching-learning arrangements on mathematical aspects of data cleaning, and investigate the structure of students' argumentation within the teaching-learning arrangements.

My research activity as a PhD student takes place within the Helmholtz Information & Data Science School for Health (HIDSS4Health), through which I am affiliated with the research groups Computational Sciences and Mathematical Methods at KIT and Radiotherapy Optimization at DKFZ. I focus on the mathematical optimization of radiotherapy planning, primarily by integrating ML-based models for TCP (tumor control probability) and NTCP (normal tissue complication probability) as biological endpoints, thereby enabling a more personalized treatment. A major component of this is the development of gradient-based, efficient optimization approaches with respect to radiomic and dosimetric variables, as well as the computational implementation in the open-source frameworks pyanno4rt and matRad.

I am a PhD student at the Collaborative Research Center 1173 in the project B9 and a part of the Computational Mathematics and Modelling for Applied Sciences (CoMMAS) group within the Scientific Computing and Mathematics Department. My research interests lie in the development and application of model-order reduction techniques to reduce computational costs for simulating kinetic equations and uncertainty quantification methods for high-dimensional problems. Additionally, I am also interested in developing HPC code and simulation frameworks.

I am a postdoctoral reseacher in the CoMMAS team since Oct. 2023. My research focuses on the deep learning approach for kinetic equations. In particular, I am interested in the Boltzmann equation. I aim at building a suggorate model for the Boltzmann collision operator, which can preserve the basic entropy dissipation property.  At the same time, it benefits from the efficiency of neural networks

As a Ph.D. researcher of the RTG 2450 in program P3, I work within the CoMMAS and SDL Materials Science groups at SCM, focusing on multiscale modeling of chemical reactions. My research integrates various computational approaches to bridge different time and length scales, with a particular emphasis on kinetic Monte Carlo simulations for capturing reaction dynamics. Additionally, I quantify uncertainties in these models to improve predictive accuracy, ensuring robust and reliable simulations for complex chemical systems.