The nAia AI Fashion Tech Platform aims to revolutionize how users discover, visualize, and shop fashion online by merging artificial intelligence, 3D garment visualization, and e-commerce into one immersive ecosystem.

The MVP (Phase 1) focuses on building a scalable web platform that enables users to:

1. Create personalized 3D avatars
2. Virtually try on garments in real-time
3. Receive AI-powered styling recommendations
4. Shop seamlessly via Shopify Headless Checkout

The ultimate goal is to deliver a personalized, data-driven, and sustainable digital fashion experience, empowering both users and designers. By leveraging AI styling intelligence, 3D visualization, and real-time engagement analytics, nAia bridges the gap between fashion creativity and cutting-edge technology positioning itself as the future of virtual fashion retail.

This project delivers an integrated AI and analytics platform for the Foundation for Sickle Cell Disease Research (FSCDR). It connects ECW (eClinicalWorks) EHR data with advanced AI-based predictive models to identify high-risk patients, automate follow-ups, and streamline state grant reporting (CMO99, CMOAE Tasks 5–13).
The goal is to transform static EHR data into actionable insights — improving research visibility, compliance, and patient outcomes while cutting down manual reporting workloads. The solution supports predictive ER-risk modeling, AI-based outreach automation, and Power BI-driven compliance dashboards across FSCDR’s Centers of Excellence.

This project focused on creating mapping reports to highlight geographic and linguistic disparities in OB/GYN service availability across Kansas. The objective was to provide visual evidence for healthcare researchers and policymakers to better understand provider distribution, identify underserved regions, and highlight language access barriers for Hispanic populations. The deliverables included static maps of provider clusters, Spanish-speaking access segmentation, and a custom symbolic visual to humanize the access challenge.