Fast and small robots, particularly flying ones, have numerous applications. However, the state-of-the-art achieves limited robustness and agility when restricted to onboard sensing. To solve this, my research seeks bioinspired perception algorithms that can be analyzed with control theory.
Currently, I am particularly interested in:
- Control using minimal egomotion representations
- Non-metric representations of environment
- Optical illusions that can be used to trick computer vision
- Early neural mechanisms responsible for perception
- State estimation utilizing specialized motion
Prior to graduate school, I studied electrical engineering at the University of Pittsburgh. There, I led an award winning aerial robotics team. After graduating, I worked as an engineer for Carnegie Robotics LLC. These experiences made me passionate about developing robust perception for robots participating in everyday life.
In response to COVID-19, I am working with Prof. Gilmer Blankenship on a creative revision of ENEE408I: Autonomous Control of Interacting Robots for Fall 2020.
The best way to contact me is through email. I enjoy answering questions!