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About

Arijit Mukherjee is a researcher whose work sits at the dynamic intersection of edge computing, robotics, and neuromorphic artificial intelligence. His research primarily focuses on enabling intelligent computation in resource-constrained environments — bringing sophisticated AI capabilities to mobile robots, embedded devices, and networked edge systems rather than relying solely on centralized cloud infrastructure. Mukherjee's most influential contribution, "Robotic SLAM" (2016, 52 citations), tackled the critical challenge of offloading computationally intensive localization and mapping tasks from energy-limited mobile robots to cloud and edge resources. This line of thinking extended naturally into his work on offloaded deep learning inference (2019, 29 citations) and fog/edge computing frameworks (2018, 18 citations), collectively shaping how practitioners approach real-time AI deployment at the network edge. His exploration of Spiking Neural Networks for gesture recognition from dynamic vision sensors (2020, 35 citations) reflects a forward-looking interest in brain-inspired, energy-efficient computation. Complementing this, his Industry 4.0 warehouse automation research demonstrated expertise in multi-agent robotics coordination and formal workflow verification. His most recent work on task-agnostic distillation for edge foundation models signals continued leadership in making large AI models viable for constrained deployments — a challenge growing increasingly central to the field.