I am a PhD student at the Robotics Institute at Carnegie Mellon University, where I work with
Michael Kaess in the the Robotics Perception Lab .
I work on statistical methods for state estimation. I am interested in developing algorithms for efficient and robust inference at the intersection of linear algebra and probabilistic graphical
I completed my Masters in Robotics at CMU working with George Kantor
on Simultaneous Localization and Mapping (SLAM) in agricultural environments.
Prior to that, I worked with Simon Lucey and
Laszlo Jeni on monocular 3D reconstruction.
In my undergrad, I majored in Electrical Engineering at the University of Maryland - College Park.
We propose a gradient-based method to
estimate well-conditioned covariance matrices for estimation. We formulate the
the learning procedure as a constrained bilevel optimization
problem over factor graphs.
We propose a gradient-based
method for learning observation
models for robot state estimation with incremental
non-differentiable optimizers in the loop. Our algorithm converges much quicker to model
estimates that lead to solutions of higher quality compared to existing methods
RACOD is an algorithm/hardware co-design for mobile robot path
planning. It consists of two main components: CODAcc, a hardware accelerator for collision detection; and RASExp, an algorithm
extension for runahead path exploration.
We develop a computer vision method to size and track the growth rates of apple fruitlets. Fruitlets are sized using
a combination of deep learning algorithms and are associated across days using a novel Attentional Graph Neural Network approach.