Mayo's Radiology Informatics AI LabProjects Led or Co-Led: - Optimized 3D Parotid Gland Segmentation on CT using Modified UNet- Optimized 3D Parotid Gland Classification on CT Using Modified DenseNet for Malignancy Detection and Tumor Classification- Automated Quantification of Cellular Features in Post-PTA AVF Outflow Vein Sections Using Modified EfficentNet v2- AI-Powered Classification of Degenerative Lumbar Spine Conditions Using MR Imaging for the RSNA 2024 Challenge- Attention-Based DenseNet for Optimized 3D Lymph Node Segmentation on CT- Quantifying Uncertainty on Race-Based BIRADs Classification for Malignant Breast Cancer.- Multimodal Dup15q Biomarker Detection for Infants - A comprehensive detector. Co-Authored: - Automated Multi-Step Abstraction of Echocardiography Reports, based on Using Privacy-Preserving Open-Source Language Models: A Comparative Study of Model Size- Automated Quantification of Cochlear Hair Cells Using Deep Learning: A Novel Approach for Enhancing Auditory Research Accuracy and Efficiency- MR-Guided Ablation for Vascular Malformations of the Plantar Foot: A Single-Center Experience- Vision to Reality: AI's Transformative Impact across the Vascular and Interventional RadiologyMentor: Dr. Ali GanjizadehPI: Dr. Bradley Erickson

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