Zero-Shot ECG Generalization using Morphology-Rhythm Disentanglement and Mamba State Space Models. Features a production-ready Clinical Dashboard
A physics-informed Deep Learning framework (Mamba/Swin-UNet) for Sentinel-1 SAR imagery denoising and speckle suppression. Features unsupervised refinement and multi-task learning.
A production-grade deep learning framework for zero-shot ECG classification that achieves state-of-the-art generalization through morphology-rhythm disentanglement and efficient long-range sequence modeling with Mamba/SSM.
Comparative analysis of Mamba vs. Transformers trained from scratch. Benchmarking Mamba's linear O(N) scaling and constant-time inference against quadratic attention mechanisms.
Computational phenomenology study of semantic satiation in neural networks. Comparing how GPT-2, BERT, and Mamba handle extreme repetition reveals causal models drift into hallucination while bidirectional models stay stable—suggesting attention directionality preserves semantic identity.
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