I am a Postdoctoral Fellow at the Robotics Institute at Carnegie Mellon University. My research bridges machine learning and robotics, with an emphasis on explainable AI for safe and effective human-robot interaction. Using principled approaches for learning models that balance accuracy and explainability, my goal is to develop robots that can explain how and why they perform actions. Machine learning models are not infallible, however, and a core part of my work is developing algorithms which enable users to leverage these explanations to apply corrective interventions when the robot makes mistakes. I have published my work at a variety of machine learning and robotics conferences, including NeurIPS, CoRL, RSS, IROS, ICRA, and many others. My research has been supported by 2 NSF EAPSI fellowships and a 4-year Dean’s fellowship from Arizona State University.
Prior to this, I…
- completed my PhD on probabilistic human-robot interaction at Arizona State University.
- spent time as a visiting researcher at Osaka University and the National University of Singapore.
- interned at several industrial research labs including Google Brain Robotics, Amazon AWS AI, and Honda Research Institute.
- earned my BS in Computer Science and MS in Computer Engineering from Arizona State University.
- worked for 5+ years as a software engineer on applications such as (semi-)autonomous vehicles, embedded systems, and healthcare applications.