Embracing Design Constraints, the Future of Fashion, and AI with Architectural Technologist Laure Michelon
The potential for data-driven design may seem endless. However, data breakthroughs within industries like architecture, fashion, and machine learning propel data usage and exploration. To further explore how data is used within a multidisciplinary setting, we connected with Los Angeles-based architectural technologist and designer Laure Michelon.
An experienced engineer turned architectural technologist, Michelon has merged her passions for architecture, fashion, and computational design into a career of exploratory work. Here interests in machine learning and the creative opportunities of GAN-based data augmentation. “Data is such a broad term,” Michelon explains. However, her extensive background in generative design and architectural technologies makes her an unmistakable force in the design industry. Although data brings endless possibilities, she never fails to approach design constraints with experimentation and the accidental opportunities data can bring.
This month, we connected with Michelon to learn more about her work, misconceptions of the design process, and the future of AI in architecture and fashion. Unpacking Data in Practice is Data Aided Design’s recurring series highlighting emerging design professionals as they share their thoughts and perspectives on data-driven design.