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
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
D. H. Shmuel, J. P. Merkofer, G. Revach, R. J. G. van Sloun, and N. Shlezinger, “SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation,” Preprint, 2023.
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 International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow
D. M. J. van de Sande, J. P. Merkofer, S. Amirrajab, M. Veta, R. J. G. van Sloun, M. J. Versluis, J. F. A. Jansen, J. S. van den Brink, M. Breeuwer, “A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow,” Magn Reson Med., 2023;1-18. doi: 10.1002/mrm.29793.
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
J. P. Merkofer, G. Revach, N. Shlezinger, T. Routtenberg, and R. J. G. van Sloun, “DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm,” Preprint, 2023.
Deep Augmented MUSIC Algorithm for Data-Driven DoA Estimation
J. P. Merkofer, G. Revach, N. Shlezinger, and R. J. G. van Sloun, “Deep augmented music algorithm for data-driven DoA estimation,” IEEE Internatioal Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022.