Qianqian Tong

Education

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

Courses Taught

• CSC 405/605: Data Science
• CSC 350: The Foundations of Computer Science II

Research

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.