Research Interests
I'm drawn to problems where rigorous machine learning meets real clinical impact in cancer care.
Multimodal AI for Oncology
Integrating clinical and imaging data (PET/CT, MRI) for prognosis and decision support.
Survival Modeling & GNNs
Graph neural networks, multi-task learning, and patient-similarity graphs for outcome prediction.
Foundation & Generative Models
Transformers, representation learning, and generative methods for data-limited medical imaging.
Explainable & Trustworthy AI
SHAP interpretability and uncertainty estimation (MC Dropout) for clinically reliable models.
Radiotherapy QA & Dosimetry
LINAC beam quality, phantom-based VMAT/IMRT QA, reference dosimetry, and audit methodology.
Quality & Safety in Radiation Medicine
Risk-based QA (FMEA), quality indicators, and large-scale analysis of international audit data.