Textile, Rewritten
Interview with Yuan Mu, Nike Innovation
Ahead of her keynote presentation at CDFAM Barcelona we interviewed Yuan Mu, Expert Computational Designer in Advanced Innovation at NIKE.
Her practice centers on computational workflows where fabrication constraints, material behavior, and design intent are woven within a single integrated system. Yuan’s background spans animation, architecture, and textile engineering, with a constant curiosity in understanding how things are manufactured and behave, rebuilding that logic systematically and computationally.
At CDFAM Barcelona, she will present a performance-oriented exploration of textile and apparel design: function-first and speculative, where computation is materially responsive rather than just visually applied.
Can you give us a brief overview of your role at Nike Innovation and what you will be covering in your CDFAM presentation?
I’m a computational designer at Design Lab within the Advanced Digital Creation Studio. My work focuses on computational design methodologies that translate complex design languages into tangible artifacts through method-driven fabrication. Some of my previous design work demonstrates that.
Today, however, I’ll be presenting a more performance-oriented exploration — one that prioritizes function over expression, and operates in a cleaner, more speculative design territory.
Complex Design
Swarovski x Nike’s Air Force 1 Collaboration
Fall/Holiday 2021
Performance Focused
Nike Flyweb Bra
June 2025
How did your background in architecture and visualization inform the way you approach textile and materials engineering?
My path into computational design started with animation. I’m a cinephile, and I was always fascinated by movement — how form transforms over time. Learning computational tools felt like learning how to choreograph motion through scripting.
During my studies, I focused heavily on digital media and computer graphics. It was an immersive world — highly controlled, highly precise. But when I moved into architecture and began working with fabrication tools, that perspective shifted.
The physical world is messy.
Materials don’t behave exactly like simulations. Tolerances matter. Things often fail. It requires negotiation, not just calculation.
Textile and materials engineering became a natural extension of that shift. Compared to architecture, textiles operate on a faster feedback loop — you can prototype, test, and iterate quickly. But they also demand intuition and sensitivity to material behavior.
So my background in visualization trained me to think in systems and motion, architecture grounded me in fabrication realities, and textiles bring both together — where computation is not just visual, but materially responsive.
The shift from analog to digitally controlled textile production is a significant one. What does that transition look like in the way in which you approach the design process?
For me, the shift from analog to digitally controlled textile production isn’t about replacing craftsmanship — it’s about decoding it.
I spend a lot of time observing analog making and collaborating with highly skilled makers who develop compelling material concepts. I stare at things in nature a lot — trying to understand how something was constructed, how the parts come together. I also intentionally travel to places that are less visible or less curated.
Today, so much of our visual input is filtered through highly edited digital platforms, and the “data” we consume often comes from distant, disconnected contexts. I try to break that cycle by seeking off-screen experiences — engaging directly with environments, materials, and processes in their raw form.
Photo collage – 3d printed biomimetic geometry
My approach is to reverse-engineer that intuition. I look for the underlying logic of creation, the constraints, the decision points — and then translate that into a structured, step-by-step system.
Once the logic is clear, it can be expanded, parameterized, and adapted within a digital workflow.
Algorithmically generated structures can produce patterns that feel organic and visually coherent. How do you think about the relationship between computational optimization and aesthetic outcome in your work?
Most of the time, I’m careful not to rely on computational language as an automatic style. Instead, I embed additional layers of parameters into the system — not just geometric variables.
These parameters allow the form to evolve from constraints rather than from a preset visual language. When tuned intentionally, optimization doesn’t just improve performance; it refines the aesthetic. The visual coherence emerges as a byproduct of logic — but it isn’t applied. It’s negotiated through the system.
Nike Zeus sunglasses, mask-like and worn close to the face, inspired by traditional acupressure points and the look of “speed” — an enduring Nike fixation. Launched July 2024.
Additive and hybrid manufacturing techniques are still relatively new territory in apparel and footwear. What does the learning curve look like, and where do you see the most promising directions?
The learning curve in this field isn’t just about tools — it’s about understanding. There’s a big difference between knowing and merely knowing of. Today, software and workflows are more accessible than ever, but accessibility doesn’t automatically mean comprehension.
Real learning happens through hands-on engagement — experimenting with software, materials, and machines, seeing the results directly, and iterating. That feedback loop is where intuition and deep understanding form.
At the same time, the increasing decoupling of tools, materials, and processes has double edge. More people can experiment and enter the field, but it’s easier for the depth of understanding to get lost. The opportunity is to design experiences that balance accessibility with rigor — where curiosity is guided, connections are visible, and digital fluency grows alongside material awareness.
I deeply value the computational design and additive manufacturing workflow that can truly be end-to-end. You can carry a concept from its earliest logic and aesthetics all the way through to final production. That continuity creates a level of cohesion that’s difficult to achieve in more fragmented pipelines.
Traditionally in product development, a designer might define a vision and then hand it off to engineering or production teams to resolve the downstream constraints. But with computational design integrated directly into additive manufacturing, you can embed manufacturing logic into the concept from the start — and continue refining it through production.
That proximity to making changes your mentality. You become more invested, more curious, and more responsible. The hands-on problem-solving mindset smooths the workflow. It does require lots of time and depth of knowledge, but I believe that shift toward integrated, end-to-end thinking is what will continue to transform the industry.
What do you hope to share with the CDFAM audience, and what are you looking to take away from the conversations there?
I hope to share a perspective on computational design as a mindset – not just a set of tools. I’m also curious about how others are building depth in an increasingly accessible landscape, moreover, how they are integrating computation, design, and manufacturing in ways that challenge my own assumptions.
Yuan’s presentation is one of a number of sessions at CDFAM Barcelona examining how computational design methodologies are being applied across scales and industries, from performance apparel to architecture and structural engineering.
The symposium brings together practitioners doing this work at depth, and the conversations that follow tend to be as valuable as the presentations themselves.
