High-Performance Data, Signal and Network Optimization Group
Contact Information
6-109 Keller Hall
University of Minnesota - Twin Cities
Minneapolis, MN 55455
TEL: (612)-625-3505
Email: mhong at umn.edu
Research Interests
Our research focus on designing and analyzing large-scale optimization methods for problems arise in various domains such as data science, machine learning/AI, signal processing and networking.
Below is a list of (more or less) theoretical topics that we have worked on.
Design and Analysis for first-order/zeroth-order, stochastic, convex and nonconvex algorithms
Design and Analysis for Momentum-Based Methods
Distributed convex and nonconvex optimization
Bi-level, Min-Max optimization problems
Analysis of equilibrium solutions for noncooperative games
Our theoretical works are greatly empowered by their applications in various engineering fields.
Alignment for Large Language Models, and Diffusion Models
Robust (Adversarial) Machine Learning
Inverse Reinforcement Learning, and Structural Estimation Problems
Differential Privacy
High-dimensional machine learning
Distributed decision making in multi-agent heterogeneous networks
Group Photos
 |
Xiangyi Graduation, May 2022
 |
Xiangyi Graduation Dinner, May 2022
 |
Group Hiking, Oct. 2022
 |
Group Escape Room, Dec. 2022
 |
Group Kayak, Sept 2023
 |
Xinwei Graduation Dinner, Nov. 2023
 |
Xinwei Defense, Nov. 2023