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.
I recently defended my PhD at TU/e on Model-Based Machine Learning for Magnetic Resonance Spectroscopy, and will be joining the University of Oxford as an NWO Rubicon postdoctoral fellow in late 2026.
News
- Apr 17, 2026 PhD thesis defended at TU/e — Model-Based Machine Learning for Magnetic Resonance Spectroscopy.
- Apr 13, 2025 Awarded NWO Rubicon personal research grant — Closing the Perception–Action Loop in MRS.
- Feb 10, 2026 Started as Postdoctoral Researcher at UMC Utrecht.
- Feb 04, 2026 Two abstracts accepted at ISMRM 2026 — #01716, #05685.
- Oct 20, 2025 Joined the MRS Code & Data Sharing Committee Executive Board (2025–2027).
Selected publications
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Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance Spectroscopy
arXiv preprint, arXiv:2505.03590, 2025.
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Strategies to Minimize Out-of-Distribution Effects in Data-Driven MRS Quantification
arXiv preprint, arXiv:2511.23135, 2025.
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WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal
NMR in Biomedicine, vol. 38, no. 6, pp. e70038, 2025.
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DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
IEEE Transactions on Vehicular Technology, vol. 73, no. 2, pp. 2771–2785, 2023.
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A Review of Machine Learning Applications for the Proton MR Spectroscopy Workflow
Magnetic Resonance in Medicine, vol. 90, no. 4, pp. 1253–1270, 2023.