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LUCID Framework

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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 then 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 the four disciplines of engineering, computer science, business, and design.


Paolo Ciuccarelli and Estefania Ciliotta

CfD Team Members

Other Team Members

Tucker Marion, Mohsen Moghaddam and Joseph Peper

PhD Students

Ryan Bruggeman, Yi (Jade) Han and Parisa Ghasemi


Northeastern University D’Amore-McKim School of Business, Northeastern University College of Engineering and the University of Michigan College of Engineering

Research Areas

Design + AI, Design + Engineering, Design + Business