I'm a robotics PhD student at Carnegie Mellon University, working in the Robot Perception Lab. My current research includes using intensity information from lidar scans to improve lidar-inertial odometry in the presence of degeneracies. I'm interested in making robust, both theoretically and at a systems level, algorithms for state estimation.

Previous research includes developing robust state estimation methods for ground vehicles using wheel encoders and cameras by estimating wheel slippage and intrinsics online. During my master's degree, I worked on the underwater robotics simulator HoloOcean as well as underwater localization using the invariant extended Kalman filter.

I graduated with an undergrad degree in applied and computational mathematics. While a bit of a unique degree for an engineer, it gives me a deep knowledge of and experience in optimization, probability theory, machine learning, algorithm design and optimal control. Paired with the real world robotics experience from working in the FRoSt Lab during my master's degree, this makes me an ideal candidate for developing algorithms for real world systems.