Mi Zhang's Profile Picture


Mi Zhang

Associate Professor
Department of Electrical and Computer Engineering
Department of Computer Science and Engineering
Michigan State University

mizhang [at] egr [dot] msu [dot] edu
(517) 432-4682
Office: 3530 Engineering Building (Map)
Lab: 1228 Engineering Building

Welcome

I am the director of the Machine Learning Systems (MLSys) Lab at Michigan State University (MSU). My students and I work on topics at the intersection of systems and machine intelligence, with current focus on On-Device AI for mobile, AR, and Internet of Things (IoT), AutoML, Federated Learning, Systems for Machine Learning, Machine Learning for Systems, and AI for health.

Short Bio
: I received my B.S. in Electrical Engineering and a minor in Computer Science from Peking University in China. I received my Ph.D. in Computer Engineering and M.S. in both Electrical Engineering and Computer Science from University of Southern California (USC) advised by Prof. Alexander Sawchuk. Before joining MSU in Fall 2014, I was a Postdoctoral Associate in Computing and Information Science at Cornell University for one year, working with Prof. Tanzeem Choudhury.


Recent Awards


Recent News

  • 07/2020:
    In collaboration with many colleagues, we are very excited to introduce FedML, a research library and benchmark for federated machine learning.
  • 07/2020:
    Invited to attend the Workshop on Federated Learning and Analytics hosted by Google AI.
  • 07/2020:
    MutualNet is accepted as an oral (top 2%) at ECCV'20.
  • 04/2020:
    Thanks Amazon AWS AI for the AWS Machine Learning Research Award!
  • 02/2020:
    Thanks Facebook Research for the Facebook Faculty Research Award on Systems for Machine Learning!
  • 02/2020:
    Our AI-enabled smart hearing aid receives the 2020 MSU Innovation of the Year Award!
  • 12/2019:
    We are the 4th Place Winner (1st Place in U.S. and Canada) of the Google MicroNet Challenge CIFAR-100 Track hosted at NeurIPS'19! Official Announcement. This is our 3rd global competition win over the past 4 years. We have made our algorithm open source, and hope it can push the research area of on-device AI forward.
  • 08/2019:
    Our AutoML work (named HM-NAS) is the Best Paper Award Nominee at ICCV'19 Neural Architects Workshop.
  • 08/2018:
    Thanks NSF for the NeTS Small grant (Co-PI) to fund our Mobile Internet of Things (Mobile IoT) project!
  • 07/2018:
    Our on-device deep learning framework (named NestDNN) that enables resource-aware multi-tenant on-device deep learning for continuous mobile vision is accepted to ACM MobiCom'18.
  • 05/2018:
    Our paper titled "The Dark Side of Operational Wi-Fi Calling Services" won the Best Paper Award at IEEE CNS'18. The work reported in this paper also won the Google Security Reward.
  • 04/2018:
    Our vision paper on realizing ubiquitous mixed reality by combining Internet of Things (IoT) and mixed reality (MR) is published at ACM SIGMOBILE GetMobile. Our ACM UbiComp'18 paper is one concrete realization of our vision.
  • 12/2017:
    Honored to be selected to serve on the Technical Advisory Board of the UCLA Depression Grand Challenge.
  • 09/2017:
    Interviewed by WIRED to share my view on NVIDIA's Deep Learning Accelerator and on accelerating deep learning on mobile and embedded devices.
  • 08/2017:
    We are the Third Place Winner of the NSF Hearables Challenge! Official NSF Announcement. We are invited by NSF to present our work at ACM UbiComp'17. Media coverage: ACM TechNews, TUN, R&D Magazine, AAU, MSU Today.
  • 07/2017:
    Our deep learning-based American sign language (ASL) translation system (named DeepASL) that enables ubiquitous and non-intrusive ASL translation at both word and sentence levels is accepted to ACM SenSys'17. Media coverage: NSF (video), MSU (video), NVIDIA, NPR (radio interview), Smithsonian, AAU, Futurity, MSU Today.
  • 05/2017:
    Our review paper on personal sensing and machine learning for digital mental health is published at the Annual Review of Clinical Psychology (Impact Factor: 12.214).
  • 04/2017:
    Honored to be selected as the 2017 NIH Mobile Health (mHealth) Scholar!
  • 02/2017:
    Our award-winning deep learning-based mobile pill recognition technology (named MobileDeepPill) is accepted to ACM MobiSys'17.
  • 09/2016:
    Thanks NSF for the PFI:BIC grant (PI)! We are very grateful for receiving this grant to develop personal sensing technologies and mobile sensor data analytics techniques to combat depression on university campuses. Media coverage: NSF (video), Smithsonian Magazine, MSU Today, EdTech, iTechPost, etc.
  • 08/2016:
    We are the First Place Winner of the NIH Pill Image Recognition Challenge! Official NIH Announcement | Media coverage: MSU Today, ABC News (TV), etc.
  • 08/2016:
    Thanks NSF for the CSR Small grant (PI)!
  • 08/2016:
    Honored to receive the NSF CRII Award! Media coverage: MSU Today, ACM TechNews, NPR, ABC News (TV), etc.
  • 05/2016:
    Our AIoT system (named AirSense) for indoor air quality sensing and analytics is accepted to ACM UbiComp'16. Media coverage: The Atlantic, MSU Today, Futurity, etc.
  • 02/2016:
    Our wireless sensing system (named BodyScan) for contactless whole-body activity and vital sign monitoring is accepted to ACM MobiSys'16.
  • 01/2016:
    Our wireless sensing system (named HeadScan) for contactless head and mouth-related activity monitoring is accepted to ACM/IEEE IPSN'16. Media coverage: Stanford Medicine, Fox News (TV interview), ReadWrite, Futurity, MSU Today, Medgadget.
  • 07/2015:
    Our device-free wireless sensing system (named DoppleSleep) for contactless sleep monitoring won the Best Paper Honorable Mention Award at ACM UbiComp'15. Media coverage: MIT Technology Review, etc.
  • 07/2015:
    Our reinforcement learning-based mobile recommendation system (named MyBehavior) is accepted to ACM UbiComp'15. Media coverage: MIT Technology Review, Mashable, MobiHealth News, etc.
  • 07/2015:
    Our paper on personal sensing for depression detection using mobile phone sensors and machine learning is accepted to JMIR. It is one of the JMIR All-Time Top Article now. See the rank here. Media coverage: TIME, CNN, TechCrunch, The Verge, CBS News, Fox News, Discovery News, Daily Mail, The Times, Newsweek, Mirror, The Telegraph, The Washington Post, The Huffington Post, Los Angeles Times, Chicago Tribune, Futurity, WebMD, US News, etc.
  • 06/2014:
    Our mobile sensing system (named BodyBeat) that listens to sounds inside human body for continuous health monitoring is accepted to ACM MobiSys'14. Media coverage: MIT Tech Review, Wall Street Journal, New Scientist.


Support

Our research is generously supported by the following federal agencies and industry partners. We express our sincere gratitude to their support.

Sponsors




© Mi Zhang. All rights reserved.