Reza Iranzad Data-verified

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

Last publication 2024 Last refreshed 2026-05-16

unknown

4 h-index 7 pubs 261 cited

Biography and Research Information

OverviewAI-generated summary

Reza Iranzad's research focuses on the application of advanced statistical learning and image processing techniques, particularly within the domain of medical imaging. He has investigated gradient boosted trees for spatial data analysis, with applications extending to medical imaging data and fluorescence intravital microscopy for detecting multicellular aggregates. His work also includes a review of random forest-based feature selection methods and their relevance to data science education and applications. Iranzad has explored multitask learning radiomics on longitudinal imaging to predict survival outcomes in non-small cell lung cancer and developed a new model for operating room scheduling. His research interests encompass tree-based ensemble statistical learning for spatial data and image processing for medical imaging.

Metrics

  • h-index: 4
  • Publications: 7
  • Citations: 261

Selected Publications

  • Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy (2024)
    1 citation DOI OpenAlex
  • Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer (2022)
    35 citations DOI OpenAlex
  • Gradient boosted trees for spatial data and its application to medical imaging data (2021)
    11 citations DOI OpenAlex

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Collaboration Network

24 Collaborators 8 Institutions 3 Countries

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