Curriculum Vitae
My research lies at the intersection of signal processing, machine learning, and biomedical data analysis. I am interested in developing learning methods that remain closely connected to the signal models and physical processes underlying the data, with a focus on inverse problems, uncertainty estimation, and interpretable inference. I currently apply these ideas to magnetic resonance spectroscopy (MRS), where I study how data-driven approaches can be combined with simulations and model-based methods. Alongside this work, I contribute to open-source tools that support reproducible and collaborative research in the MRS community.
Employment
Postdoctoral Fellow
University of Oxford, Oxford, United Kingdom
- NWO Rubicon personal research grant: Closing the Perception–Action Loop in MRS.
- Adaptive, closed-loop MRS acquisition framework using active inference to iteratively optimize pulse sequences based on expected information gain.
Postdoctoral Researcher
UMC Utrecht, Utrecht, The Netherlands
- AI-based modality translation model from 4D-MR angiography to time-of-flight imaging.
Education
Ph.D. in Electrical Engineering
Eindhoven University of Technology, Eindhoven, The Netherlands
- Research focus: Bayesian deep learning, uncertainty quantification, physics-informed inference, array signal processing, and open-source tool development for reproducible MRS research.
- Thesis: Model-Based Machine Learning for Magnetic Resonance Spectroscopy.
M.Sc. in Electrical Engineering and Information Technology
ETH Zurich, Zurich, Switzerland
- Specialization: signal processing, machine learning, and artificial intelligence.
- Thesis: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm.
B.Sc. in Electrical Engineering and Information Technology
ETH Zurich, Zurich, Switzerland
- Foundational techniques in communications and systems engineering.