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About

Ketan Rajawat is a researcher whose work sits at the intersection of online learning, time-varying optimization, and distributed algorithms, with particular emphasis on applications in robotics and multi-agent systems. His most influential contribution, "Online Learning With Inexact Proximal Online Gradient Descent Algorithms" (2019, 109 citations), addresses the challenge of nondifferentiable dynamic optimization under real-world computational constraints—a problem central to robotics and subspace tracking. Building on this foundation, his work on tracking moving agents via inexact online gradient descent (2018, 39 citations) demonstrates how collaborative multi-agent systems can perform complex tasks like search-and-rescue and intrusion detection despite the limitations of individual robots. Rajawat has also advanced distributed optimization over dynamic graphs, designing algorithms capable of handling intermittently connected sensor and robotic networks—critical for real-world deployments. His foray into nonparametric online learning explores how streaming machine learning can move beyond linear models while maintaining memory efficiency and consistency. Additionally, his work on autonomous calibration of wheeled mobile robots highlights a broader commitment to practical robotics applications. Across his portfolio, Rajawat consistently bridges theoretical rigor with computational practicality, making his research highly relevant to students working in optimization, distributed systems, and autonomous robotics.