Research projects in AI for healthcare, neural signal processing, and clinical decision support.
The Python Implementation of Waveclause with artifacts removal.
A generative and interpretable framework for predicting disease trajectories using transformer architectures.
An open-source, modular framework for automated spike sorting in human intracerebral recordings.
SHAP-based temporal risk pathways and LLM-driven natural language explanations for clinical prediction models.
Patient-specific computational models for real-time risk stratification and dynamic intervention planning.
Leveraging large language models for clinical text understanding, synthetic data generation, and retrieval-augmented clinical applications.
