Optimization
This group develops mathematical and computational tools to solve complex decision-making problems under uncertainty. Their work spans theory and applications, with contributions to optimization, machine learning, control systems, and intelligent decision support across engineering and data-driven domains.
Themes:
- Stochastic optimization
- Applications in machine learning and AI
- Distributed and large-scale algorithms
- Engineering systems and control
- Applied operations research and decision analytics
Members
- Jianqiang Cheng - Optimization under Uncertainty, Conic Optimization, and their applications
- Neng Fan - Methodologies in Optimization; Applied Operations Research; Data Mining and Machine Learning
- Erfan Yazdandost Hamedani - Methodologies in Optimization: Saddle point problems, Distributed Optimization, Bilevel Optimization.Applications: Machine Learning, Data Science, Artificial Intelligence
- Afrooz Jalilzadeh - design, analysis, and implementation of stochastic approximation methods for solving convex optimization and stochastic variational inequality problems with applications in machine learning, game theory, and power systems.
- Jian Liu - integration of manufacturing engineering knowledge, control theory and advanced statistics for product quality and productivity improvement. His recent research focuses on system prognostic/diagnostic modeling and analysis