RiSE
Research in Information Science for Engineering
Research in Information Science for Engineering
We perform empirical studies on human subjects that advance the scientific understanding of human cognition, human behavior, and team dynamics in design, and the influence of social factors on engineering. For example, our studies provided the first empirical evidence of cognitive chunking during freehand sketching. We also discovered the dimensions of product similarity used as the basis of design-by-analogy.
We develop formal languages, representations, and algorithms to support computer-aided modeling and reasoning in design, especially in early-stage systems design. For example, our decade-long research produced a novel language for modeling technical systems in terms of their functions. With this language, designers can model complex systems to analyze their feasibility, perform automated solution synthesis and search, analyze the effects of state changes in the system, and analyze cause-and-effect relations.
We develop end-usable tools for automating design and manufacturing processes for small and large companies. For example, our research created a CAD-based expert system for evaluating the technical feasibility and economic viability of using additive manufacturing in mass production, in a project funded by major US-based automakers. Our current research is producing generative CAD-based expert systems that automatically design grippers for locating and immobilizing sheet metal parts for manufacturing and assembly operations.