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About

Akarsh Prabhakara is a researcher specializing in millimeter-wave (mmWave) radar sensing, machine learning-based perception, and autonomous robotic systems. His work sits at the intersection of signal processing, computer vision, and robotics, with a particular focus on enabling robust sensing in challenging environmental conditions where traditional modalities like LiDAR and cameras fall short. Prabhakara's most influential contribution, "High Resolution Point Clouds from mmWave Radar" (2023, 69 citations), introduced a machine learning pipeline that dramatically improves the perception quality of single-chip mmWave radar systems — a breakthrough for robotic mapping, odometry, and localization in degraded environments such as fog, smoke, and dust. His follow-up demonstration, RadarHD, further validated this super-resolution approach, showcasing LiDAR-like outputs from radar hardware. His work on radar-camera fusion extended these capabilities to long-range depth imaging up to 300 meters, with applications in surveillance and geo-fencing. Beyond radar perception, Prabhakara has applied autonomous sensing to real-world ecological challenges, developing a multi-agent reinforcement learning framework to facilitate whale rendezvous using autonomous robots and synthetic aperture radar. This interdisciplinary reach highlights his ability to translate core sensing innovations into impactful, domain-spanning applications.