Qianqian Tong

Qianqian Tong is an Assistant Professor in the Computer Science department at the University of North Carolina at Greensboro. Her research focuses on stochastic optimizations, sparse learning, federated learning, and privacy-preserving machine learning algorithms. She has developed novel methods for efficient optimization in deep learning and federated learning frameworks, including communication-efficient distributed algorithms and decentralized systems.

Education:

• Ph.D. Computer Science and Engineering, University of Connecticut
• M.S. Computational Mathematics, Zhengzhou University
• B.S. Mathematics, Zhengzhou University

Research interests include designing algorithms for sparse learning, federated learning with privacy guarantees, and applying deep graph learning to drug discovery. Recent projects involve tensor-based models for multidimensional data analysis and improving convergence in ADAM optimization.

Her publications span optimization theory, federated learning systems, and molecular modeling applications. Though no awards are listed, her work demonstrates significant contributions to efficient machine learning methodologies.

Teaching includes advanced courses on data science and computer science foundations. No lab affiliations or grant details are explicitly mentioned in the provided text.