Yan Huang

Yan Huang (黄炎) is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Division of Decision and Control Systems. He earned his Ph.D. in Control Science and Control Engineering from Zhejiang University (2020-2024) under Prof. Jinming Xu and Prof. Jiming Chen, with prior degrees from Southeast University and Anhui University. His work focuses on distributed optimization and machine learning.

• Ph.D. in Control Science and Control Engineering, Zhejiang University, 2020-2024
• M.S. in Control Science and Control Engineering, Southeast University, 2017-2020
• B.S. in Automation, Anhui University, 2013-2017

Yan Huang specializes in distributed and federated optimization theory, applying these techniques to large-scale machine learning and networked control systems. His research addresses data heterogeneity, robustness to failures, and computational efficiency in decentralized networks. He has developed flexible frameworks for gradient tracking and personalized optimization.

His recent publications emphasize stochastic gradient tracking, variance reduction, and differential privacy in distributed settings. Key topics include adaptive stepsizes, Byzantine-robust algorithms, and computation-communication trade-offs, reflecting his focus on scalable and secure optimization methods.

• Outstanding Graduate, Zhejiang University, 2024
• Outstanding Doctoral Student, Zhejiang University, 2022 and 2023
• 2nd prize in China Post-graduate Mathematical Contest in Modeling, 2017
• Outstanding Graduate, Anhui University, 2017

Huang has taught courses on Principles of Automatic Control, Modern Control Theory, and Distributed Control and Optimization. He has also presented at ICML 2022 and Zhejiang University's academic forum, highlighting his work on decentralized SGD and data heterogeneity.