Textile Engineering
Material behavior, testing standards, evidence, and apparel knowledge.
About / How I got here
I didn’t move from one field to another. I learned how to translate between them.
I studied Textile Engineering before completing an MFA in Art & Technology / Sound Practices. Textile engineering taught me to work with material behavior, testing, and evidence. Art and sound taught me to observe, research, and build embodied experiences.
Today, I bring those ways of thinking into product work. I study the context—the people, information, materials, and constraints—then use product judgment and AI-assisted prototyping to turn what I learn into testable tools, systems, and experiences.
Background
Experience & education
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Mana Contemporary Chicago
Creative Product Technologist · Contract
Designing tenant-facing digital tools that translate artist and staff needs into clearer information architecture, request workflows, and responsive web experiences.
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School of the Art Institute of Chicago
Graduate Teaching Assistant
Technical support for student projects, teaching support, and studio operations.
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Uster Technologies (Suzhou)
Material Analysis Intern
Yarn and fiber testing with Uster Tester 6 and Tensorapid 5; standardized ISO/ASTM protocols and technical documentation.
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School of the Art Institute of Chicago
MFA in Art & Technology / Sound Practices
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Soochow University
Bachelor’s Degree in Textile Engineering
Foundations
What each field taught me
Art & Sound
Field research, spatial experience, physical systems, and artistic judgment.
Product & AI
Workflow design, information architecture, rapid prototyping, and evaluation.
Selected evidence
Three projects,
one practice.
Different domains ask different questions. Across them, I keep returning to the same responsibility: understand the context, define a useful structure, and verify what the system can actually support.
- Mana Web Platform Designed a clearer information and request experience for an artist-tenant community.
- FitFabric Connected garment measurements, material behavior, product-page evidence, and AI-assisted fit reasoning while making uncertainty visible.
- Chicago Art Index Defined a review-first cultural information system separating discovery, evidence, human judgment, and public publishing.
AI practice
AI, authorship,
and accountability.
AI accelerates implementation. I remain responsible for defining the problem, making product decisions, and evaluating the result.
- The problem and intended users
- Product direction and priorities
- Information architecture
- Constraints and acceptance criteria
- Implementation alternatives
- Code generation and refactoring
- Test scaffolding
- Technical documentation
- Workflow and interaction quality
- Evidence and source integrity
- Edge cases and failure states
- Whether the result matches the intended product