معرفی
Yangyang Xu is an Associate Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI), where he conducts research at the intersection of optimization theory and computational methods. His work bridges theoretical foundations with practical applications in machine learning and data science.
Dr. Xu's research focuses on several interconnected areas:
- Continuous optimization: specializing in stochastic programming, distributed optimization, and tensor optimization
- Optimization methods: developing stochastic approximation techniques, first-order methods, block update methods, and operator splitting approaches
- Computational frameworks: advancing parallel and distributed computing implementations for large-scale optimization problems
- Applications: applying optimization techniques to machine learning, signal processing, compressed sensing, and data mining challenges
His recent publications (2023-2025) demonstrate a clear progression toward increasingly sophisticated optimization frameworks, particularly in decentralized and stochastic settings. Xu has made significant contributions to nonconvex optimization, minimax problems, and constrained optimization, with his work showing strong theoretical foundations coupled with practical implementations.
Dr. Xu serves on the editorial board of Mathematics of Operations Research (since January 2023), reflecting his standing in the optimization community.
He actively mentors six PhD students and has successfully guided multiple students to completion, with graduates moving to positions at organizations including Evonik, SAS, and UT Austin. His teaching includes graduate courses such as Stochastic Optimization and Reinforcement Learning and Introduction to Optimization.
Dr. Xu leads a research group focused on advancing optimization theory while developing practical algorithms for real-world applications, particularly in distributed computing environments where communication efficiency is critical.
