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ECE Departmental Seminar

AIOT for Precision Medicine

Qingxue Zhou
Purdue University

Friday, 8/18/23, 2:00pm
Light Engineering 250 

Abstract: Precision medicine is being advanced greatly by IoT and AI technologies that are essential for big data capturing and mining. This talk will firstly target the big data capturing challenge with the massive-device medical IoT system, and introduce a deep reinforcement learning framework that can orchestrate the energy, computing, communication and task distribution configurations. The extension of this study to wearable data capturing will also be demonstrated. Secondly, the theories to advance AI mining capabilities will be introduced, based on a neuromorphic algorithm that enables spiking neural learning with backward adaptation of synaptic efflux, and a graph neural network algorithm that mines brain dynamics with multi-channel multi-view co-learning. This talk, through the selected studies, will give an introduction of the efforts on AIOT for precision

Bio: Dr.  Zhang has over fifteen years’ experience in academia, industry, and medical, with his postdoc research at Harvard, products R&D in ICT, and Ph.D. research at University of Texas at Dallas. He started the assistant professor position in 2018, at Purdue School of Engineering & Technology, IUPUI. He is interested in medical AI and medical IoT/wearable technologies. He is a recipient of the NSF CAREER Award, has over 50 journal/conference publications, and serves as NSF/NIH/NIST Panelists, as well as chair/committee for multiple IEEE Conferences/workshops.