I am a recent PhD graduate from the School of Interactive Computing at Georgia Tech, where I was advised by Prof. Frank Dellaert.
My research goal is to build robotic systems that reason probabilistically under uncertainty over geometry to enable efficient and reliable autonomy in the real world.
Currently, I am focused on proprioceptive state estimation of legged robots using concepts from machine learning and nonlinear control. Robot dynamics are intuitive constraints since they obey nature's laws, thus being able to model the observable and the latent processes underlying legged locomotion, we can extract useful information about the robot's state without the need for exterioceptive sensors such as cameras.
I am also a maintainer of GTSAM, an industrial-strength factor graph library for robotics, as well as its sister projects (e.g. GTDynamics).