About Me

I am a PhD student at the Biomedical Diagnostics Lab (BM/d) at the Electrical Engineering department of the Eindhoven University of Technology (TU/e). My research interests revolve around the intersection of signal processing and machine learning. Particularly in the development of physics-based deep learning methods that leverage the model-agnostic nature of neural networks and the interpretability of traditional model-based techniques. In my current research, I focus on developing and applying such hybrid systems to magnetic resonance spectroscopy.

Publications

  • Deep Root Music Algorithm for Data-Driven DoA Estimation

    D. H. Shmuel, J. P. Merkofer, G. Revach, R. J. G. van Sloun, and N. Shlezinger, “Deep Root Music Algorithm for Data-Driven Doa Estimation,” IEEE Internatioal Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.