Deepak Vasisht

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About

Deepak Vasisht is a pioneering researcher at the intersection of wireless sensing, robotics, and machine learning, with work that pushes the boundaries of how radio frequency (RF) signals can enhance perception and autonomy in intelligent systems. His research explores millimeter-wave radar for robotic navigation, demonstrated through *Radarize* (2024), which advances SLAM capabilities using generalizable Doppler-based odometry — enabling robots to navigate reliably in challenging conditions like poor lighting and occlusions where optical sensors fail. This work has already garnered 13 citations, reflecting its immediate relevance to the robotics community. Vasisht also tackles critical challenges in distributed machine learning for robotics, with *Fed-EC* (2024) proposing a bandwidth-efficient federated learning framework for autonomous visual navigation — addressing privacy and communication bottlenecks in real-world deployments. His *RF-Annotate* system (2022) further showcases his interdisciplinary creativity, leveraging wireless tags to automate image annotation for object detection, bridging RF sensing and computer vision in a practically impactful way. Collectively, his contributions demonstrate a consistent vision: using wireless technologies to make autonomous systems smarter, more private, and more resilient — a research agenda with growing significance as robotics and IoT converge.