PhD Thesis Defense
PhD Student
Robotics Institute,
Carnegie Mellon University

Trustworthy Learning using Uncertain Interpretation of Data

GHC 6501

Abstract: Motivated by the potential of Artificial Intelligence (AI) in high-cost and safety-critical applications, and recently also by the increasing presence of AI in our everyday lives, Trustworthy AI has grown in prominence as a broad area of research encompassing topics such as interpretability, robustness, verifiable safety, fairness, privacy, accountability, and more. This has created [...]

PhD Thesis Defense
PhD Student
Robotics Institute,
Carnegie Mellon University

Whisker-Inspired Sensors for Unstructured Environments

NSH 4305

Abstract: Robots lack the perception abilities of animals, which is one reason they can not achieve complex control in outdoor unstructured environments with the same ease as animals. One cause of the perception gap is the constraints researchers place on the environments in which they test new sensors so algorithms can correctly interpret data from [...]