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.
Tell us about your academic and professional background.
I have an M.S. in Architectural Technologies from SCI-Arc and a B.S. in Civil Engineering from Columbia University. Before SCI-Arc I was working as an energy analyst and facade consultant at Glumac, an MEP firm. Currently, I am the assistant teacher for the architectural technologies program at SCI-Arc and I have worked on a few projects for Ishida Rehm Studio that have shown at FRAC, the Pompidou, and The University of Texas at Austin School of Architecture.