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The opinions, findings, and conclusions or recommendations expressed are those of the Center author(s) and do not necessarily reflect the views of the National Science Foundation.

Center Overview

The Center for e-Design is a cooperative research center to accelerate product realization by developing and applying intelligent strategies and tools that integrate, leverage, and enable the processes, personnel, and data of design and manufacturing enterprises.

The Center for e-Design integrates innovation and creativity with fundamental principles of science, mathematics, and engineering in the development, testing, and implementation of new methods and technologies for the design of products and systems focused on faster time-to-market, better product quality, and significantly reduced costs. It focuses its efforts in three major areas to deliver value to its industry and government partners:

  • Fundamental research focuses on creating methods, tools, and technologies to address industry needs in e-tools-enabled product development and realization, including integrating design information and knowledge; enhancing information structure; enhancing design innovation through collaborative design and manufacturing; and incorporating intelligence into the product development life cycle.
  • Research test bed focuses on the integration of interdisciplinary research activities to validate developed tools, methods, and technologies.
  • Engineering education and technology transfers focus on educating a new generation of engineers and scientists proficient in e-design and rapidly transferring results into usable applications for industry and government.


  • Northeastern University
  • University of Connecticut
  • University of California, Davis
  • University of Cincinnati
  • University of Texas, Dallas
  • University of Virginia
View Center Website

Center Personnel

Research Focus

Design education

The objective of design education research is to prepare and support students and practitioners to take on current and future challenges in engineering design. Research focuses on improving design pedagogy through training on new design tools and methods, integrating design thinking into engineering design processes, and providing training on and understanding decision-making under uncertainty in local, distant, and distributed learning environments. The ultimate goal is to prepare individuals for careers in intelligent product and system design, development, and realization.

Information infrastructure

The objective of information infrastructure research is to explore and develop new software, hardware, information modeling approaches (e.g., ontologies), and secure data handling processes and storage necessary to improve the product development processes that corporations and their supply chains use.


The objective of innovation research is to develop new engineering design processes and methodologies to improve creativity and innovation in the design of products, systems, production systems, and supply chains. This research focuses on collaboration tools, measuring and improving creativity in the designs of products and service systems, and social-centric design processes.


The objective of integration research is to develop new methods and tools that more closely integrate engineering design with engineering analysis, manufacturing, supply chain, maintenance, sustainability, and the end user. Research focuses on the integration of information, knowledge, tools, and models across the product life cycle to enable innovative designs, decreased time to market, and reduced total life cycle costs.


The objective of intelligence research is to develop methods that enable better and faster decision-making through intelligent, data/information-driven, and model-based systems. Research in this area focuses on the development of tools, methods, and algorithms that enhance capabilities of the designers through collaboration with computing resources such as artificial intelligence, cloud-based digital design, data mining, sensor and sensor networks data science, and machine learning.