Mingyi Zhou

Mingyi Zhou Min-E Chow

PhD Candidate

Monash University

Biography

I am currently a third-year PhD student in HumaniSE lab, Monash University in Australia, where I have the privilege of being mentored by Prof Li Li, Prof John Grundy, Prof Chunyang Chen, and Dr Xiao Chen (in a sequence of the length of supervision). I was a PhD student in Monash SMAT Lab, leading by Prof Li Li. My current research project is focused on using SE techniques such as program analysis to enhance the reliability of deployed machine learning (ML) systems, especially for smart mobile apps. This includes assessing the risks associated with ML systems, protecting their intellectual property, and minimizing their attacking surface. Additionally, I am exploring ways to optimize machine learning programs on mobile devices to improve inference efficiency.

Interests
  • Software Engineering for AI
  • Responsible AI Deployment
  • AI Compiler
Education
  • PhD, 2021-now

    Monash University, Australia

  • Master of Engineering

    University of Electronic Science and Technology of China (UESTC)

  • Bachelor of Engineering

    Wuhan University of Technology, China

News

🎉 Received the Google Conference Scholarship! Thanks Google!
🎉 One paper has been accepted by ISSTA'24!
🎉 Received a nomination for FIT Education Excellence 2023!
🎉 One paper has been accepted by AAAI'24!
🎉 One paper has been accepted by ICSE'24!

Slected Publications

Quickly discover relevant content from here.
(2024). Model-less Is The Best Model: Generating Pure Code Implementations to Replace On-device DL Models (Just accepted). In ISSTA'24 (Just Accepted).

PDF Cite Code

(2024). Investigating White-Box Attacks for On-Device Models. In ICSE'24.

PDF Cite Code DOI

(2024). Concealing Sensitive Samples against Gradient Leakage in Federated Learning. In AAAI'24.

PDF Cite Code DOI

(2023). ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-Based Systems. In ISSTA'23.

PDF Cite Code DOI

(2020). DaST: Data-free Substitute Training for Adversarial Attacks. In CVPR'20 (Oral).

PDF Cite Code DOI

Professional Services

Reviewer:

  • ACM Computing Surveys
  • IEEE Transaction on Software Engineering
  • IEEE Transaction on Image Processing
  • IEEE Signal Processing Letters
  • Knowledge-Based Systems
  • Conference on Computer Vision and Pattern Recognition (CVPR)
  • International Conference on Computer Vision (ICCV)
  • European Conference on Computer Vision (ECCV)
  • Asian Conference on Computer Vision (ACCV)