Research Interest

Research: Quantitative Marketing, Generative Models, Creativity, Aesthetic Design

Methods: Field Experimentation, Deep Learning

Guided Creativity: AI Intermediation for Enhancing Originality and Quality in Visual Design

Joint work with Artem Timoshenko and Guy Aridor

Creative fixation is a common side effect when designers seek inspiration from success ful designs, often limiting the originality of subsequent work. This paper introduces AI intermediation, a novel approach that leverages generative models to overcome this challenge. Our approach creates variations of leading designs that maintain core se mantic concepts while differing visually, and provides these variations to the designers instead of the original exemplars. This allows communicating valuable insights and inspiring novel interpretations without inducing fixation. We empirically validate the proposed approach using a field experiment involving professional designers in a logo design contest. Results show that designers with AI intermediation produce (1) higher quality work than those with no exposure to exemplars, and (2) more-original work than those with direct exposure to exemplars. We further decompose the sources of creativity and demonstrate that while the generative model yields distinct variations, human creativity remains pivotal for improving originality and quality of the final de signs. Consequently, AI intermediation presents an efficient facilitator to human-driven creative process. 

Using Context-Aware Generative AI to Close Cross-Cultural Design Gap

The globalization of brand marketing has intensified cross-cultural visual design challenges, as large brands expand across regions while small brands increasingly outsource design work to international designers. Both scenarios require an understanding of regional cultural conventions that may be unfamiliar to designers. We introduce a machine learning approach that enables designers to overcome cultural and perceptual distances in visual brand communication. The proposed approach recognizes authentic regional design conventions through trademark data analysis, then calibrates generative models using these insights. The validation involves consumer evaluation studies with brand logos, where participants both within and outside targeted regions assess design effectiveness.