Siddharth Garg

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About

Siddharth Garg is a leading researcher at the intersection of millimeter-wave (mmWave) sensing, autonomous navigation, and adversarial machine learning. His work focuses on enabling robots and autonomous vehicles to perceive and navigate complex environments using high-frequency wireless signals. Garg’s major contributions include pioneering mmWave-based positioning for target localization, where he leverages the high angular and temporal resolution of these signals to achieve precise navigation in cluttered indoor spaces. His 2022 paper on mmWave-assisted robot navigation has garnered 20 citations, establishing a foundation for robust, signal-driven path planning under uncertainty. In a striking 2019 study (19 citations), Garg demonstrated the vulnerability of deep neural networks in autonomous systems by creating adaptive adversarial videos on roadside billboards that can dynamically alter vehicle trajectories—a critical finding for safety in self-driving cars. His 2023 work on path planning to localize mmWave sources (4 citations) further advances efficient estimation algorithms using Extended Kalman Filters. Through these contributions, Garg has shaped the discourse on both the promise and perils of integrating wireless sensing with AI-driven autonomy.