Artificial Intelligence in Products Engineered for X (AiPEX) Lab
Our research explores the use of machine learning methods that predictively improve the outcome of product design solutions through the acquisition, fusion and mining of large-scale, publicly-available data. It has been reported that 70-80% of the costs of a product are determined during the design phase. Here, the term product is used in a general sense to refer to physical/digital systems that are guided by user needs, and that require domain knowledge to create. Towards enhancing the efficiency of the design process and creating personalized solutions, our research focuses on three thrust areas, outlined below.
Our lifelong research and education goals are tightly integrated and focus on advancing personalization, both from a design and learning perspective.