I am a PhD candidate studying at the University of Toronto Robotics Institute at ASRL with Tim Barfoot. Broadly speaking, my research goals involve bringing advanced theoretical tools to bear on the pressing challenges of robotics. Namely, my interests lie in the areas of nonlinear and convex optimization and how they can be applied to robustly and efficiently solve robotics problems.

To date, my research has focused on the development of certifiably correct algorithms that solve key problems in robotics (particularly in state-estimation). These algorithms leverage convex relaxations in order to either globally solve a given problem or verifiy correctness of a given solution. More recently, I have been looking at how certifiable algorithms can be applied to optimization layers that are embedded in deep-learning networks.

Click here for more info about my current streams of research.

Background

Before starting my PhD in 2021, I worked for 5 years at MacDonald Detwiller and Associates (MDA) as a Controls/Systems Engineer working on orbital manipulators. In 2016, I completed a Master’s in Applied Science at the University of Toronto Department of Electrical and Computer Engineering, studying patterned linear control with Mireille Broucke. I completed my undergraduate degree in Engineering Science at the University of Toronto in 2014.