Mr. Minxi Feng, the University of Sydney, Australia
Minxi Feng is a Ph.D. student in Computer Science at the Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney (USYD). Supervised by Dr. Wei Li and Prof. Albert Zomaya, Minxi’s research focuses on online algorithms, online scheduling, machine-learned prediction, online pattern recognition, Cyber-Physical Systems (CPS), and digital twins. He holds multiple advanced degrees, including an M.S. in Data Science and an M.S. in Financial Mathematics from the University of Sydney and the University of New South Wales, respectively, as well as a B.A. in International Trading and Economics from the University of Science and Technology Beijing.
Professional Profile:
Summary of Suitability for Best Researcher Award:
Minxi Feng demonstrates strong potential for the Best Researcher Award through a combination of rigorous academic training, innovative research, and substantial contributions to the field of computer science. His research interests in online algorithms, online scheduling, machine-learned prediction, and digital twins are highly relevant to current advancements in computing.
Education:
- Ph.D. in Computer Science
Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney (USYD)
Current student
- M.S. in Data Science
School of Computer Science, University of Sydney, Sydney, Australia
- M.S. in Financial Mathematics
School of Mathematics and Statistics, University of New South Wales, Sydney, Australia
- B.A. in International Trading and Economics
School of Economics and Management, University of Science and Technology Beijing, Beijing, China
Work Experience:
- Personal and Institutional Investment Consultant Manager
Fuzhou Road Branch, Shenwan Hongyuan Securities Company, Shanghai
August 2019 – December 2019
- Provided advice and technical analysis to personal investors
- Managed institutional investments in the securities market
Publication top Notes:
Online demand peak shaving with machine‐learned advice in digital twins