Qianqian Tong

Picture of Qianqian Tong

Assistant Professor, 2023
Ph.D. in Computer Science, University of Connecticut (2022)

Office: Petty 152

Research: Machine Learning; Stochastic Optimization; Deep Learning; Differential Privacy; Sparse Learning; Graph Convolutional Network (GCN) and Federated Learning
Teaching: Data Science


Dr. Tong’s research interests span the areas of stochastic optimizations, sparse learning, federated learning, and privacy-preserving machine learning. Dr. Tong mainly developed new machine learning algorithms, such as efficient sparse learning algorithms, parallel stochastic second-order algorithm, efficient Adam algorithms, and federated learning algorithms. Her goal is to develop efficient and privacy-preserving optimization algorithms for deep learning and federated learning, including communication-efficient distributed algorithms, decentralized algorithms, and federated algorithms. Other recent projects have designed a new deep graph learning method to improve drug discovery & precision medicine; and propose a tensor-based model with quadratic inference function to analyze multidimensional data.


Dr. Tong’s research has been supported by the National Science Foundation (NSF) and the National Institutes of Health (NIH).


Dr. Tong joined UNCG in March 2023 as an assistant professor. Before joining UNCG, she obtained her Ph.D. degree in Department of Computer Science from University at Connecticut, UCONN in December 2022, M.S. and B.S. in Mathematics from Zhengzhou University.