My research at the Bosch Center for Artificial Intelligence focuses on the development of safe active learning methodology in the context of Real-Driving Emissions. Safe active learning methodology supports the engine calibration process by suggesting experiments that maximize information gain while satisfying safety constraints. The general guideline for my research is: recognize the application's challenges, develop solid mathematical methods in response and return tailored solutions.
I obtained my PhD from the Heidelberg university in interdisciplinary mathematics and collaborated with systems biologists and epidemiologists during my postdocs at the center for modeling and simulations in the biosciences in Heidelberg and Yale School of Public Health.
My research interests include Calibration, Parameter Identifiability, and Stochastic Processes.
My Research Fields
- Gaussian Processes
- Design of Experiment
- Active Learning
- Safe Active Learning
- Dynamics Modeling