Atiyya Shaw

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Dr. Shaw’s research program innovates around data and methods for prioritizing user-centered outcomes in transportation engineering and urban planning. She applies a range of analytical approaches (e.g., statistical matching, transfer learning) to: (1) address biases and other shortcomings present in active (e.g., survey data) and passive (e.g., GPS location data, marketing data, etc.) data; and (2) to integrate across passive and active data with the aim of expanding and improving the information available for planning and designing urban systems. On the methods side, her work brings advanced and under-utilized approaches from psychometrics (e.g., item response theory) and econometrics (e.g., decomposition approaches) to study and forecast infrastructure system user behavior.

Accomplishments and Awards


Research Highlights