Adaptive Sensors for Reliable Intelligence in Resource-Constrained and Unreliable Environment
11am on Tuesday March 18th
Join in ITE 401 or via Webex
Abstract: The demand for intelligent, energy-efficient sensors continues to grow in the era of edge computing and autonomous systems. However, the increasing complexity of these systems demands innovative solutions that effectively balance accuracy, energy efficiency, and robustness. This talk focuses on redefining sensors as active, adaptive systems through cross-layer innovations spanning hardware and software. Central to this is the development of uncertainty-based early warning generators, which integrate cross-layer uncertainty estimation to enable real-time adaptation. By dynamically adjusting operations based on environmental conditions, these sensors enhance reliability and energy efficiency, achieving robust performance in safety-critical and resource-constrained settings. The talk will conclude with an outlook on future research directions, emphasizing collaborative sensor networks and scalable frameworks for adaptive, context-aware operation in complex, dynamic environments.
Bio: Minah Lee is currently a postdoctoral researcher in the School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology, under the supervision of Prof. Saibal Mukhopadhyay. She holds a PhD (2022) and MS (2019) in Electrical Engineering from the Georgia Institute of Technology, and a BS (2017) in Creative IT Engineering and Electrical Engineering from Pohang University of Science and Technology (POSTECH). Her research interests include adaptive sensor design, hardware-aware machine learning, energy-efficient edge computing, and autonomous and multi-agent system applications.
For more information, contact: Brandy Ciraldo at brandy.ciraldo@uconn.edu