Past talk · AI/ML Seminar Series
Propagating Uncertainty from Modeling into Decision-Making for Trustworthy Autonomy
Postdoctoral Scholar, Department of Computer Science, Stanford University
- Date & time
- Monday, January 24, 2022 · 1:00 PM
- Location
- Online (live stream)
Abstract
Autonomous agents such as self-driving cars have gained the capability to perform individual tasks such as object detection and lane following in simple, static environments. While advancing robots towards full autonomy, it is important to minimize deleterious effects on humans and infrastructure. For robots to safely operate in the real world, it is vital to quantify the multimodal aleatoric and epistemic uncertainty around them and use that uncertainty for decision-making. In this talk, I discuss how we can leverage approximate Bayesian inference, kernel methods, and deep neural networks to develop interpretable autonomous systems for high-stakes applications.
About the speaker
Ransalu Senanayake is a postdoctoral scholar in the Statistical Machine Learning Group at Stanford University, working with Prof. Emily Fox and Prof. Carlos Guestrin. He focuses on making downstream applications of machine learning trustworthy by quantifying uncertainty and explaining decisions. He obtained his PhD in Computer Science from the University of Sydney.