I'm interested in mathematical challenges arising from biomedical imaging applications that have a classical inverse problem described by partial differential equations at their core. As such, my work draws from various fields of applied mathematics, including Bayesian inference, variational regularization, compressed sensing, computational optimization, deep learning and numerical analysis.
The main applications I currently work on are computed tomography (CT), photoacoustic tomography (PAT), ultrasound tomography (UST), electro- and magnetoencephalography (EEG/MEG) and magnetic resonance imaging (MRI ).
After a first degree in mathematics and physics in 2011, I studied for a PhD in applied mathematics in WWU Münster (Germany), which included a research visit at UCLA. From September 2014 to October 2017, I worked as a postdoc at UCL, after which I joined CWI as a tenure track researcher.