Research area + Impact Engine
LUCID Framework | National Science Foundation (NSF) Grant
From User Reviews to User-Centered Generative Design
Automated Methods for Augmented Designer Performance.
This project imagines user-centered design processes where the latent needs of myriad users are automatically elicited from social media, forums, and online reviews, and translated into new concept recommendations for designers. This project will advance the fundamental understanding of if and how AI can augment the performance of designers in early-stage product development by investigating two fundamental questions: (1) Can we build and validate novel natural language processing (NLP) algorithms for large-scale elicitation of latent user needs with cross-domain transferability and minimal need for manually labeled data? (2) Can we build and validate novel deep generative design algorithms that capture the visual and functional aspects of past successful designs and automatically translate them into new design concepts? Our convergence research team is well-positioned to undertake these questions, with expertise across four disciplines of engineering, computer science, business, and design.
Team members: Mohsen Moghaddam, Lu Wang, Tucker Marion, Paolo Ciuccarelli, Estefania Ciliotta
Partners: Northeastern College of Engineering, D’Amore-McKim School of Business at Northeastern, University of Michigan