Stanford MLSys Seminar Episode 41: Mi Zhang [Th, 1.35-2.30pm PT]

[Karan Goel]

Hi everyone,

We're back (after ICLR!) with the forty-first episode of the MLSys Seminar on Thursday from 1.35-2.30pm PT. 

We'll be joined by Mi Zhang, who will talk about building machine learning systems for edge devices. The format is a 30 minute talk followed by a 30 minute podcast-style discussion, where the live audience can ask questions.

Livestream Link: https://www.youtube.com/watch?v=xy4sbZ4ev2k

Guests: Mi Zhang

Title: Empowering the Next Billion Devices with Deep Learning

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, we 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. First, we 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. We 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, we 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. The results and insights obtained in those works are also useful in designing many other modern machine learning systems.
Bio: 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 systems and machine learning, spanning areas including On-Device AI, Automated Machine Learning (AutoML), Federated Learning, Systems for Machine Learning, and Machine Learning for Systems. 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 six best paper awards and nominations. He is also the recipient of the Facebook Faculty Research Award, Amazon Machine Learning Research Award, and MSU Innovation of the Year Award.

See you all there!

Best,

Karan