I am a machine learning researcher with 5+ years experience in deep learning, specializing in self-supervision and scalability.
[1] Nabil Jabareen, et al. “Segmenting brain tumors in multi-modal MRI scans using a 3D SegNet architecture.” International MICCAI Brainlesion Workshop. Cham: Springer International Publishing, 2021.
[2] Nabil Jabareen, et al. “ISImed: A Framework for Self-Supervised Learning using Intrinsic Spatial Information in Medical Images.” arXiv preprint arXiv:2410.16947 (2024). UNDER REVIEW
[3] Nabil Jabareen, et al. “Anisotropic Fourier Features for Positional Encoding in Medical Imaging.” Accepted in MICCAI Workshop on Shape in Medical Imaging. Cham: Springer Nature Switzerland, 2025.
[4] Nabil Jabareen, et al. “Learning to scale: Deriving Data-Driven Scaling Laws for ECG-Optimized CNNs.” Accepted in European Society of Cardiology (ESC) 2025
[5] Nabil Jabareen, Maximilian Zillekens, et al. “From Clinic to Couch: An Uncertainty-Aware Deep Learning Approach for ECG Analysis Across Modalities” Accepted in European Society of Cardiology (ESC) 2025
[6] Nabil Jabareen, Florian Herzler, et al. “Generalization and Calibration Capabilities of Residual Neural Networks with Stochastic Weight Averaging for Chagas Disease Predicting” Accepted in Computing in Cardiology (CinC) 2025