View course details:
https://online.stanford.edu/courses/aa228v-validation-safety-critical-systems
When autonomous systems make high-stakes decisions, how do we know they're safe?
Join Sydney Katz, a postdoctoral researcher in the Stanford Intelligent Systems Laboratory, for an overview of the methods used to build safety cases for AI-powered decision-making systems operating in high-stakes environments. She will cover validation approaches applicable across multiple sectors, including aviation, autonomous driving, and finance.
You'll discover how safety engineers and AI developers establish trust in complex systems that rely on machine learning—systems where traditional testing alone may not be sufficient. Whether you're interested in developing autonomous technologies, implementing AI in regulated industries, or making decisions about deploying these systems, this webinar will help you understand the importance of safety validation methods.
Sydney Katz, PhD
Sydney Katz is a postdoctoral researcher at Stanford's Intelligent Systems Lab. Her research focuses on the design and validation of safety-critical decision-making systems and builds upon ideas from optimization, Bayesian inference, uncertainty quantification, neural network verification, and reachability analysis.