Mingyi Hong

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Mingyi Hong

Assistant Professor
Electrical and Computer Engineering
University of Minnesota
6-109 Keller Hall
University of Minnesota, Minneapolis, MN 55455
Google Scholar citation
Biographical Sketch, [Curriculum Vitae]
Email: mhong at umn.edu

Education

  • Ph.D., Systems and Information Engineering, University of Virginia, VA, 2011

  • M.S., Electrical Engineering, Stony Brook University, NY, 2007

  • B.S., Electrical Engineering, Zhejiang University, China, 2005

Professional Experience

  • Assistant Professor, Dept. ECE, University of Minnesota, MN, 2017-present

  • Assistant Professor, Dept. IMSE, Iowa State University, IA, 2014-2017

  • Research Associate/Assistant Professor, Dept. ECE, University of Minnesota MN, 2013-2014

  • Post-Doctorate Fellow, Dept. ECE, University of Minnesota MN, 2011-2013

Research Interests

My research focus on designing future generation of networks including wireless and energy networks, as well as developing and analyzing large-scale optimization methods for big data problems.

Below is a list of (more or less) theoretical topics that we have worked on.

  • Design and Analysis for first-order convex and nonconvex algorithms

  • Distributed convex and nonconvex optimization

  • Computational complexity for problems in signal processing and communications

  • Approximability for interference management problems

  • Stochastic optimization for large-scale problems

  • Analysis of performance for semi-definite relaxation (SDR) based algorithms

  • Analysis of equilibrium solutions for noncooperative games

Our theoretical works are greatly empowered by their applications in various engineering fields.

  • Cross-Layer resource management for next-generation complex networks

  • High-dimensional machine learning

  • Optimization for smart power grids

  • Intrusion detection and state estimation for cyber-physical systems

  • Speech denoising and enhancement

  • Distributed decision making in multi-agent heterogeneous networks

Teaching

Office Hour (Fall 2017)

  • Wed. 10:00 am—11:00 am at Keller 6-109

  • By appoitment

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