My research lies at the interface of control theory, robotics, and machine learning, with a focus on state estimation, nonlinear filtering, and geometric methods for dynamical systems. I design structure-aware algorithms that combine mathematical rigor with practical performance, enabling reliable perception and navigation for autonomous systems, particularly those relying on inertial sensors.
My work has contributed to invariant filtering theory and to learning-enhanced estimation methods, with results deployed in robotic platforms, including one state-estimation algorithm embedded in the industrial aircraft displayed above. More broadly, I seek to bridge fundamental systems theory with industrial applications through sustained collaborations with engineers and industrial partners.