Events Calendar

Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

Speaker: Mi Zhang , Machine Learning Systems Lab at Michigan State University

Talk Title: Empowering the Next Billion Devices with Deep Learning

Series: Center for Cyber-Physical Systems and Internet of Things

Abstract: The proliferation of edge devices and the gigantic amount of data they generate make it no longer feasible to transmit all the data to the cloud for processing. Such constraints fuel the need to move the intelligence from the cloud to the edge where data reside. In this talk, I will present our works on how we bring the power of deep learning to edge devices to realize the vision of Artificial Intelligence of Things (AIoT).

First, I will present our work on designing adaptive frameworks that empower AI-embedded edge devices to adapt to the inherently dynamic runtime resources to enable elastic on-device AI. Second, we shift from the single edge device setting to the distributed setting for the task of distributed on-device inference. I will focus on one killer application of edge computing, and present a distributed workload-adaptive framework for low-latency high-throughput large-scale live video analytics. Third, I will present our work on designing a distributed on-device training framework that significantly enhances the on-device training efficiency without compromising the training quality. Lastly, I will talk about our work on developing automated machine learning (AutoML) techniques to address the device deluge challenge which acts as one key barrier of achieving the vision of AIoT.

Biography: Mi Zhang is an Associate Professor and the Director of the Machine Learning Systems Lab at Michigan State University. He received his Ph.D. from University of Southern California and B.S. from Peking University. Before joining MSU, he was a postdoctoral scholar at Cornell University. His research lies at the intersection of mobile/edge/IoT systems and machine intelligence, spanning areas including On-Device/Edge AI, Automated Machine Learning (AutoML), Federated Learning, Systems for Machine Learning, Machine Learning for Systems, and AI for Health and Social Good. He has received a number of awards for his research. He is the 4th Place Winner of the 2019 Google MicroNet Challenge, the Third Place Winner of the 2017 NSF Hearables Challenge, and the champion of the 2016 NIH Pill Image Recognition Challenge. He is the recipient of seven best paper awards and nominations. He is also the recipient of the National Science Foundation CRII Award, Facebook Faculty Research Award, Amazon Machine Learning Research Award, and MSU Innovation of the Year Award.

Host: Pierluigi Nuzzo and Bhaskar Krishnamachari

Webcast: https://usc.zoom.us/webinar/register/WN_p5OEJlPxQlakO4hqovuGEQ

Location: Online

WebCast Link: https://usc.zoom.us/webinar/register/WN_p5OEJlPxQlakO4hqovuGEQ

Audiences: Everyone Is Invited

Contact: Talyia White