Brown CS News

“Generative models,” says Brown CS faculty member Adriana Schulz, “have transformed our ability to create images, geometry, and other digital content. But designing objects for the physical world requires more than generating something plausible or visually compelling. Physical artifacts must also be manufacturable and satisfy functional and performance requirements, turning design into a search through complex, highly constrained spaces.”

Earlier this month, Adriana delivered a keynote address (“Designing for the Physical World”) at the 34th Pacific Conference on Computer Graphics and Applications (Pacific Graphics 2026), the annual flagship conference of the Asia Graphics Association. It’s widely considered a premium platform for researchers and practitioners to share the latest advances in computer graphics and interactive techniques.

It’s tempting, Adriana explains, to think that generative models will absorb the challenge of these complex, highly constrained spaces without the need for explicit optimization, but she argues the opposite: optimization is not merely still relevant, it’s becoming more powerful.

“The recent success of AI is, at its core, a success of large-scale optimization,” Adriana says, “and it has dramatically expanded what practical optimization can do. But harnessing this power requires rethinking how we represent designs – not treating representations as fixed and building optimizers around them, but instead designing representations for optimization. Through examples from computational design and digital fabrication, I will show how this perspective can make difficult inverse problems tractable and enable new tools for creating objects that work in the physical world.”

Adriana’s research group creates design tools and systems that aim to revolutionize how we build physical artifacts. A central challenge for design tools in manufacturing is the need to simultaneously nurture the creative ability to conceive novel designs and the analytical capacity to critically evaluate and optimize functionality and production. Her group tackles this challenge through innovative solutions that are grounded in the fundamentals of geometry processing and combine insights from machine learning and programming languages.

Adriana’s recent honors include include a CRA’s Anita Borg Award, a Sloan Foundation Fellowship, MIT Technology Review's Innovators Under 35 award, an NSF CAREER Award, and the 2024 ACM SIGGRAPH Significant New Researcher Award.

For more information, click the link that follows to contact Brown CS Communications Manager Jesse C. Polhemus.