Reza Iranzad Source Confirmed

OverviewAI-generated summary

Reza Iranzad's research focuses on developing and applying statistical learning and image processing techniques, particularly within the realm of medical applications. His work has explored gradient boosted trees for analyzing spatial data, with specific applications to medical imaging datasets such as FDG-PET scans. Iranzad has also investigated multitask learning radiomics on longitudinal imaging to predict survival outcomes for non-small cell lung cancer patients undergoing risk-adaptive chemoradiation.

His research interests extend to tree-based ensemble methods for spatial data and image processing for medical imaging. Iranzad has contributed to the literature with publications on structured adaptive boosting trees for detecting multicellular aggregates in fluorescence intravital microscopy and a review of random forest-based feature selection methods. He also has research experience in operating room scheduling optimization.

Metrics

• h-index: 4
• Publications: 7
• Citations: 221