"My research is very interdisciplinary. One current research question, for example, is: How can curvature information be extracted from microscopic image data? I am interested in what meaningful mathematical building blocks or patterns can be used to approximate a signal or an image? What essential information can be extracted by breaking down the signal or image into such basic patterns? The aim is to enable users to read more from their data using my mathematical methods.
Another new aspect of my research, which initially found its way into my teaching via SKILL.de, is the question: What methods can we use to optimise learning in mathematics and empower learners to measure their success independently and reliably? We are developing and evaluating new tools for this task for my student teachers, i.e. prospective math teachers.”
More on her Research
Professor Brigitte Forster-Heinlein's research focuses on the following topics:
- Harmonic analysis: wavelet analysis, complex wavelets, phase information, complex splines
- Integral transforms for signal and image analysis
- Approximation theory, sampling theorems and interpolation
- Function theory: Anharmonic Fourier and Dirichlet series, growth behavior of whole functions of special type
- Applications in signal and image processing