Towards Data-Capable Engineers

Cause-Source framework for uncertainty in engineering

Project Features

  • Developing theoretical frameworks to analyze uncertainty
  • Designing instruments (interviews and surveys) to measure and understand engineering behavior
  • Conducting interviews to gather behavioral data
  • Designing and testing educational interventions to improve engineers’ understanding of uncertainty

Project Description

Engineers are responsible for delivering safe, efficient solutions. For instance, automobile manufacturers need to design cars that are light enough to be gas-efficient, but still sturdy enough to protect the passenger. A complication in this design process is uncertainty: No engineer can predict with 100% confidence what a driver will do with their car, or what conditions it will encounter. Traditionally, engineers handle uncertainty by “overdesign”—making things heavier than they need to be. However, scientists from other disciplines (such as statistics) have more efficient ways to handle uncertainty. A better understanding of uncertainty—and how engineers themselves react to it—will lead to safer, more efficient engineering designs. Achieving these efficiency gains is critical for solving important issues, such as technological responses to anthropogenic climate change. This project will study how real engineers react to uncertainty and train them to handle it more effectively.

Students working on this project will conduct interviews, perform mixed-methods analysis, help run workshops, and learn about the fundamental nature of uncertainty.

Related Publications

Zachary del Rosario
Zachary del Rosario
Assistant Professor of Engineering and Applied Statistics

Empowering scientists and engineers to reason under uncertainty