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About

Shumon Koga is an emerging researcher whose work bridges autonomous robotics, control theory, and active perception. His research centers on two interconnected domains: active exploration and mapping for mobile robots, and safety-critical control for partial differential equation (PDE) systems. In the realm of autonomous robotics, Koga has made notable contributions through his development of iterative Covariance Regulation (iCR), a principled optimal control framework enabling robots to actively explore and map environments by minimizing map uncertainty over continuous SE(3) trajectories. This work, along with his active SLAM research and learning-based continuous control policies for information-theoretic perception, demonstrates a sustained effort to make autonomous systems both more capable and mathematically rigorous. His pursuit-evasion work further extends these ideas into adversarial sensing scenarios. On the control theory side, Koga has pioneered the application of high-relative-degree Control Barrier Functions (CBFs) to PDE backstepping control, particularly for thermodynamic Stefan models with actuator dynamics — a technically demanding and practically relevant contribution to safety-critical systems that has attracted 12 citations since 2022. With a growing portfolio of interdisciplinary publications spanning robotics, control, and machine learning, Koga represents a promising voice in the formal methods and autonomous systems research community.