BayesiaLab
Potential Emerging Hazards in Vehicle Safety

Potential Emerging Hazards in Vehicle Safety

Presented at the 10th Annual BayesiaLab Conference on Friday, October 28, 2022.

Abstract

GM’s experience shows that expanded data access must be coupled with tools that can manage volume, techniques that appropriately control for exposure and sample bias, and processes that incorporate SME knowledge. BayesiaLab helps GM address these critical issues, with one tool, in a way that is intuitive and makes communication with engineers easier to support vehicle hazard investigations.

About the Presenter

Michelle Michelini is currently a Senior Technical Fellow in the Global Planning Analytics team at General Motors, where she focuses on projects which require the deployment of new analytical methods and tools to develop deeper insights and recommendations from Planning data. Michelle started her career as a statistician with GM Credit Card and then worked for OnStar as a strategist. From OnStar, she went to GM Vehicle Safety, where she developed the Vehicle Safety Analytics team as a response to the findings of the ignition switch investigation. She holds a Bachelor of Science from the University of Michigan in Mathematics and Applied Statistics and a Master of Science from Carnegie Mellon University in Information Technology and Systems.


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