My Projects

From FDA-regulated medical devices to homelab infrastructure

Q-Life System FDA Class II medical device platform

Q-Life System (QLS) — FDA Class II Medical Device Platform

A comprehensive software platform for continuous physiological monitoring, supporting real-time collection of ECG, respiratory rate, skin temperature, activity, sleep, and social engagement data from a wearable medical device. Designed to support 1,000+ concurrent users, 500+ data packets/second, and 7-year data retention for FDA audit requirements.

Technologies

Java 17Spring BootPostgreSQLKotlinAndroid SDKHIPAAFDA 21 CFR Part 11
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Multi-sensor Android research application for biomedical data collection

Multi-Sensor Android Research App

Android application collecting physiological data from 10+ body sensors for biomedical research at SDSU Research Foundation. Custom data parsing pipeline for real-time ingestion and downstream research analysis.

Technologies

AndroidKotlinSensor IntegrationData Pipelines
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Homelab server setup with multiple services with high availability

Personal Homelab Infrastructure

Self-hosted server infrastructure running network services, monitoring, and experimental deployments. Deployed and manage a virtualized environment hosting over 10 different services. Configured secure VPN tunnels, NAS server with ZFS for data redundancy safeguarding over 2TB of files.

Technologies

ProxmoxDockerKubernetesWireGuardTrueNASPi-Hole
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Kubernetes cluster visualization with 3x Dell 7010

Kubernetes Cluster System

Implemented a high-availability Kubernetes cluster using NixOS and K3s across a 3-node Mini PC setup. Ensured robust service availability and network load balancing for containerized applications with MetalLB. Managed the deployment and automated scaling of 12 containerized applications.

Technologies

K3SNixOSMetalLBKubernetes
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Smart Pet Bowl system with AI-powered pet recognition and water monitoring

Smart Pet Bowl - Team P.A.W.S

Senior Design project that enhances pet health monitoring by automating water intake tracking. The system uses an ESP32, Grove AI Vision Module with custom YOLOv8 model, ultrasonic sensors, and Raspberry Pi Zero W to detect, identify, and quantify pet hydration. Features real-time pet recognition, water consumption tracking, and web dashboard for monitoring.

Technologies

ESP32YOLOv8Machine LearningRaspberry PiIoTComputer VisionPCB Design
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Stock management mobile application with NFC integration

Stock Management Mobile Application

Developed a cross-platform stock management app for iOS and Android, currently in beta with over 50 users. Integrated near-field communication (NFC) technology to streamline item tracking, reducing inventory logging time by 60%. Designed an intuitive and accessible user interface, focusing on ease of use for non-technical end-users.

Technologies

EASReact NativeXCodeAndroid StudioNFC
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