
I am an Assistant Research Professor at DAMTP, and a member of the Cambridge Hub for Innovative Mathematics in Research and Applications CHiMIRA.
I completed my PhD at the University of Klagenfurt, and have since held postdoctoral positions at the University of Graz and the Max Planck Institute for Solar System Research.
My research lies at the intersection of regularization theory, nonlinear dynamical inverse problems, and data-driven methods. I was awarded the GIP Prize on Inverse Problems for the best PhD thesis in inverse problems across German-speaking countries (2020–2022).
Broadly, I explore:
- Regularization and novel reconstruction: all-at-one, bi-level, one-shot strategies
- Partial differential equations: nonlinear, time-dependent, well-posedness
- Inverse problems in PDEs: parameter estimation, model discovery
- Machine learning: data-driven physics, discretization by neural networks
- Data assimilation: real-time estimation, model reference adaptation
- Passive imaging: random media, correlation-based techniques
My work is inspired by a range of applications:
- Medical imaging: magnetic particle imaging, Landau-Lifshitz-Gilbert eq.
- Cell biophysics: traction force microscopy, hyperelasticity, active force densities, Stokes equations
- Helioseismology: solar differential rotation, viscous-inertial wave modeling
- Reaction-advection-diffusion: hidden nonlinear laws, Fisher eq., Lane-Emden eq., Zeldovic-Frank-Kamenetskii eq.
- Aeroacoustic: source detection, optimal experimental design
- Heat phenomena: inverse heat source, optimal sensor placement
Publications
Bi-level regularization via iterative mesh refinement for aeroacoustics
(2024)
Sequential bi-level regularized inversion with application to hidden reaction law discovery
(2024)
Bi-level iterative regularization for inverse problems in nonlinear PDEs
– Inverse Problems
(2024)
40,
045020
(doi: 10.1088/1361-6420/ad2905)
Inferring solar differential rotation and viscosity via passive imaging with inertial waves
(2024)
Bi-level iterative regularization for inverse problems in nonlinear PDEs
(2023)
Learning-Informed Parameter Identification in Nonlinear Time-Dependent PDEs
– Applied Mathematics & Optimization
(2023)
88,
76
(doi: 10.1007/s00245-023-10044-y)
Discretization of parameter identification in PDEs using neural networks
– Inverse Problems
(2022)
38,
124007
(doi: 10.1088/1361-6420/ac9c25)
Parameter identification for elliptic boundary value problems: An abstract framework and applications
– Inverse Problems
(2022)
38,
075005
(doi: 10.1088/1361-6420/ac6d02)
Learning-informed parameter identification in nonlinear time-dependent PDEs
(2022)
On numerical aspects of parameter identification for the Landau-Lifshitz-Gilbert equation in Magnetic Particle Imaging
– Inverse Problems and Imaging
(2022)
16,
89
(doi: 10.3934/ipi.2021042)
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