Haoze Ni
I study people, turn signals into ideas, and shape clearer experiences across marketing, design, and technology.
Blended portrait view.
A little about me00/ About
I’m Alex Nee. My work moves between marketing and design—sometimes I’m reading the numbers, sometimes I’m shaping the experience, and usually I’m doing a bit of both.
01/ Projects
Each project begins with a person, a tension, or a question. Open any project note for the context, the thinking, and the small choices that gave the work its character.
A ski-day companion that connects planning, local creators, photo matching, and the memories that remain after the lifts close.
A day on the mountain
Find your people, your photos, and a day worth keeping.
Swipe the image · Tap to zoom
A study of how nostalgia, parasocial interaction, and familiar characters shape connection across the GBH Kids audience.
More project notesHover to preview · Click to read
01 / Mobile product · 2026
Explore the interface
Slopeframe brings the practical and emotional parts of skiing into one calm flow: check the mountain, find a photographer, identify your photos, and collect each trip without losing the moment to admin.
02 / Audience research · GBH Kids
The work translated academic theory into an audience framework that could explain not only what viewers remembered, but why that relationship might still influence attention, affection, and willingness to share.
03 / Independent practice · Ongoing
04 / Product + data operations
The interface supports quick weekly reading without flattening the analysis. Teams can filter by period, target-price band, buyer type, or product; update source data in batches; configure the KPI deck; notice anomalies; then move from a date-focused view into rolling comparisons and product-level detail.
05 / Game design + HCI
06 / Interactive web + motion
02/ Publications
A multi-source verification framework that checks generated news summaries against source documents, Wikipedia, and web evidence before making conservative corrections.
A local-random-walk-enhanced label propagation method for discovering communities within cultural and creative IP marketing networks.
Combines multimodal sensing and temporal learning to move fall monitoring toward personalized, interpretable, and privacy-aware risk prediction.
Uses skeleton sequences and multi-dimensional attention to support accurate fall detection in a lightweight architecture designed for real-time CPU deployment.
Reviews AI-enabled home eldercare with attention to monitoring, usability, interoperability, privacy, ethics, and human-centered interaction.