🎓 Biomedical Engineer focused on advancing AI-driven medical imaging and clinically meaningful healthcare technology. 🔬 I develop and evaluate deep learning pipelines for explainable diagnosis, MRI analysis, and quantitative biomarker estimation.

• Built an end-to-end PyTorch pipeline for pediatric pneumonia detection from chest X-rays using a fine-tuned ResNet-18 backbone.
• Achieved AUROC = 0.979, AUPRC = 0.985, with Sensitivity = 0.997 (screening) and Precision = 0.94 (rule-in mode).
• Applied Grad-CAM visualizations to highlight pulmonary opacities, ensuring transparency and clinical interpretability.
• Implemented temperature scaling and Youden’s J threshold tuning for probability calibration and operating-point flexibility.
• Emphasized explainability, robust evaluation, and reproducible design for research-grade medical imaging AI.

• Built a 3D U-Net conditional GAN to synthesize CMRO₂ maps from multimodal quantitative MRI (CBV, CBF, T2, T2*).
• Preprocessing included GM masking, MNI152 resampling, and normalization.
• Achieved high accuracy with all modalities (MSE ~0.0007, SSIM ~0.92, Pearson r ~0.95).
• Demonstrated that vascular modalities (CBV & CBF) are essential for reliable CMRO₂ estimation.

• Co-developed SugarIQ, an AI-enabled diabetes management platform built in 48 hours, featuring real-time patient monitoring, health trend visualization, medication tracking, and AWS-powered live consultation workflows (Transcribe Medical).
• Contributed to synthetic data generation and patient modeling using BRFSS health indicators, enabling realistic patient datasets for analysis; platform built with React + TypeScript, AWS Lambda, API Gateway, S3, and Python-based data engineering.

✨ “Advancing healthcare through imaging, AI, and biomedical engineering.”