UC Berkeley

During early-stage design processes, designers must navigate significant uncertainty and make sense of abstract, multi-dimensional goals (e.g., function, aesthetics, ergonomics), eventually synthesizing them into design outcomes. Data-driven design is a paradigm that aims to leverage data and computational methods to support decision making, allowing designers to surpass cognitive limits (e.g., idea fixation). However, concepts fundamental to decision making during early-stage design (e.g., ‘What are similar design ideas?’ and ‘Will the design reflect dependability?’) are ill-defined, cognitively complex, and not well-represented by computation. Therefore, a key challenge is to align computational representations with how humans perceive and process information, enabling designers to accurately express their intent. To address this challenge, my dissertation research explores behavioral studies and computational techniques to understand and quantify representations (both cognitive and reflected within design artifacts) of these complex concepts throughout the design process. First, I demonstrate how function can be quantifiably compared across engineered systems and products, and how human perceptions of similarity align. Then, I show how intangible semantic prompts (e.g., dependable, versatile, comfortable) can be tangibly reflected in designs, by humans and through human-in-the-loop computation. The insights derived from this work contribute to human-centered computing for early-stage design, enabling designers to more easily and effectively design innovative products.