Prince Singh

Papers

Total Citations

H-Index

About

Prince Singh is a leading researcher at the intersection of control theory, sensor scheduling, and neuromorphic engineering. His work fundamentally addresses how to optimize sensing under severe resource constraints—a critical challenge for autonomous systems, robotics, and networked control. Singh’s most influential contribution, his 2017 paper on “Supermodular mean squared error minimization for sensor scheduling in optimal Kalman Filtering” (22 citations), provides a groundbreaking framework for selecting which sensors to activate under a limited energy budget to minimize estimation error. By proving the problem’s supermodular structure, he enabled the use of efficient, near-optimal greedy algorithms for sensor scheduling, directly impacting applications from drone navigation to industrial monitoring. Complementing this, his 2016 work on “Stabilization of linear continuous-time systems using neuromorphic vision sensors” (12 citations) pioneers the use of event-based, asynchronous vision sensors for control. This research demonstrates how the high temporal resolution and low latency of neuromorphic sensors can stabilize dynamical systems, opening new avenues for agile robotics where traditional frame-based cameras fail. Singh’s work is notable for bridging rigorous theoretical guarantees with practical, hardware-driven solutions, making him a key figure in the future of resource-aware, high-performance control systems.