Biomedical Engineering
Medical device software, health data analysis, biomedical signal processing, and clinical data applications with regulatory guidance.
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EvidenceLens: A Self-Hosted Biomedical Search Engine with Hybrid Ranking and Conflict-of-Interest Transparency
EvidenceLens unifies 20+ fragmented medical literature sources into a single hybrid-ranked interface, surfaces author conflict-of-interest badges from public CMS Open Payments data, and runs answer synthesis entirely on the user's own inference key — zero recurring backend cost.
VitalCheck API: Building a Multi-Domain Health Intelligence Engine with Python, FastAPI, and Production-Grade ML
VitalCheck is a production-deployed REST API that delivers real-time health risk assessments across ten medical and wellness domains. Built with Python, FastAPI, and a suite of machine learning models, it provides a unified interface for clinical and biometric predictions — from diabetes and stroke risk to MRI-based brain tumor classification.
Building a Low-Cost Digital Spirometer with Arduino & MPX2010
Learn to design a high-precision digital spirometer using an Arduino Nano and MPX2010 pressure sensor. This engineering guide covers analog signal processing, circuit integration, and algorithms for calculating Forced Vital Capacity (FVC).
Sleep Lab Monitoring System: Real-Time CPAP Data Analytics for Critical Care
A full-stack medical monitoring system that enables real-time collection, analysis, and visualization of CPAP data for sleep apnea patients in clinical sleep labs.
EOG Sleep Mask: Detecting Secondary Sleep Strokes in Real-Time
A wearable, real-time stroke detection system disguised as a comfortable sleep eye mask. By utilizing electrooculography (EOG) to track eye movement and an onboard microcontroller to perform real-time signal processing, the device acts as an automated sentinel.
Trading Precision for Speed: Implementing Model Quantization and Format Conversion for Clinical Medical Imaging
In clinical environments, deploying deep learning models for medical imaging—brain tumors, strokes, cardiac anomalies—presents a unique constraint: the inference latency and memory footprint directly impact patient outcomes.