Akul Datta

Vaughn College of Aeronautics and Technology, University of Illinois Urbana-Champaign

Papers

Total Citations

H-Index

About

Akul Datta is a researcher working at the cutting edge of cloud robotics, wireless communications, and machine learning-driven perception systems. His work addresses a critical challenge in modern robotics: how computationally constrained robots — such as low-power drones, space rovers, and subterranean exploration vehicles — can efficiently transmit high-bitrate sensory data, including video and LIDAR streams, to remote compute servers for real-time inference and decision-making. Datta's most significant contributions center on the co-design of communication and machine inference pipelines, developing frameworks that intelligently balance bandwidth constraints with the demands of remote perception tasks. His 2021 paper on co-design for cloud robotics, among his most cited works, introduced approaches that optimize data transmission specifically for downstream machine learning objectives rather than traditional reconstruction quality metrics. His complementary work on task-relevant representation learning further advances this paradigm by ensuring that only perceptually meaningful information is prioritized during transmission. With citations accumulating across both conference and journal venues, Datta's research has garnered growing recognition within the robotics and communications communities. His work is particularly valuable for students and engineers seeking to build smarter, bandwidth-efficient robotic systems operating in communication-limited environments.