Chris Hayner
Co-advised with Behcet Acikmese
Chris is a graduate student in the Aeronautics & Astronautics Department. He is broadly interested in guidance, navigation, and perception. His research vision is to couple the flexibility of learning-based methods of state estimation with the robustness and provability of convex optimization-based methods of guidance for safety-critical applications (e.g. Powered-Descent Guidance, Human-Robot Interactions). His specific areas of interest are:
- Integrating contextual information into computer vision methods: Using non-vision-based sensors and data to aid computer vision methods to enable agents to make informed decisions in dynamic environments.
- Advancing multi-modal sensor fusion methods: Introducing computer vision methods to efficiently use multiple modalities to ensure robustness in uncertain and adverse environments.
- Perception constraints for real-time optimization-based trajectory planning: Formulating convex constraints to optimally use visual-based sensors in performing real-time environment-aware trajectory planning for autonomous agents.
Chris is currently a NASA Space Technology Graduate Research Opportunities (NSTRGO) fellow.