Amit Ranjan Trivedi
University of Illinois Chicago, University of Illinois Urbana-Champaign
Papers
9
Total Citations
73
H-Index
4
About
Amit Ranjan Trivedi is a prominent researcher at the intersection of edge computing, uncertainty quantification, and autonomous robotics systems. His work addresses one of the most pressing challenges in modern AI deployment: enabling resource-constrained edge devices to make reliable, uncertainty-aware decisions in safety-critical environments. Trivedi's most impactful contribution, MC-CIM (32 citations), introduced a compute-in-memory framework leveraging Monte Carlo dropouts to bring Bayesian inference capabilities to low-power edge hardware — a significant breakthrough for applications where mispredictions carry serious consequences. Building on this foundation, he has pioneered lightweight uncertainty-aware methods for visual odometry, multimodal 3D object detection, and LiDAR perception, directly targeting platforms like insect-scale drones and surgical robots where computational budgets are severely limited. His STARNet framework further extends this vision by addressing sensor trustworthiness and anomaly recognition in complex autonomous systems employing LiDAR, RADAR, and event cameras. More recently, Trivedi has explored generative approaches to ultra-efficient sensing and uncertainty-calibrated deep reinforcement learning for mission-critical robotics. Collectively, his research establishes a coherent and impactful agenda: making autonomous edge intelligence not merely accurate, but robustly aware of its own limitations — a quality essential for real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 2Lightweight, Uncertainty-Aware Conformalized Visual Odometry13 citations · 2023
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