Prof. Bao Wang

Department of Mathematics

University of Utah, USA

Email: [email protected]

Qualifications

2016 Ph.D. Applied Mathematics, Michigan State University, USA

2012 B.S. Mathematics, Suzhou University, China

Publications (Selected)

1. Fan Jia, Yuhao Huang, Shih-Hsin Wang, Cristina Garcia Cardona, Andrea L. Bertozzi, and Bao Wang, “Plug-and-Play Image Restoration with Flow Matching: A Continuous Viewpoint”, SIAM Journal on Imaging Sciences, Accepted, (2026).
2. Fan Jia*, Yuhao Huang*, Bao Wang, “Generalized Proximal Langevin Algorithms via Backward Differentiation Formula”, Communications on Pure and Applied Analysis (CPAA), Accepted, (2026).
3. Wenqi Tao*, Huaming Ling*, Zuoqiang Shi, Bao Wang, “Deep Learning with Data Pri-vacy via Residual Perturbation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted, (2025).
4. Zhicong Liang, Bao Wang, Quanquan Gu, Stanley Osher, Yuan Yao, “Differentially Private Federated Learning with Laplacian Smoothing”, Applied and Computational Harmonic Analysis, Vol. 72, 101660, (2024).
5. Wes Whiting, Bao Wang, Jack Xin, “Convergence of Hyperbolic Neural Networks under Riemannian Stochastic Gradient Descent”, Communications on Applied Mathematics and Computation, Vol. 6, pp. 1175-1188, (2024).
6. Tao Sun, Qingsong Wang, Yunwen Lei, Dongsheng Li, and Bao Wang, “Pairwise Learning with Provably Convergent Adaptive Online Gradient Descent”. Transactions on Machine Learning Research, ISSN: 2835-8856, (2023).
7. Justin Baker, Elena Cherkaev, Akil Narayan, and Bao Wang, “Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs”, Journal of Scientific Computing, 95(2), 54, (2023).
8. Mengqi Hu, Yifei Lou, Bao Wang, Ming Yan, Xiu Yang, Qiang Ye, “Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum”, Journal of Scientific Computing, 95:33, (2023).
9. Bao Wang, Qiang Ye, “Improving Deep Neural Networks Training for Image Classification with Nonlinear Conjugate Gradient-style Adaptive Momentum”, IEEE Transactions on Neural Networks and Learning Systems, doi:10.1109/TNNLS.2023.3255783, (2023).
10. Tao Sun, Dongsheng Li, Bao Wang, “On the Decentralized Stochastic Gradient Descent with Markov Chain Sampling”, IEEE Transactions on Signal Processing, doi:10.1109/TSP.2023. 3297053, (2023).
11. Zhemin Li, Tao Sun, Hongxia Wang, and Bao Wang, “Adaptive and Implicit Regulariza-tion Neural Network for Matrix Completion”, SIAM Journal on Imaging Sciences, 15 (4), 2000-2022, (2022).
12. Tao Sun, Dongsheng Li, and Bao Wang, “Decentralized Federated Averaging”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45 (4), 4289-4301, (2022).
13. Yifan Hua*, Kevin Miller*, Andrea L Bertozzi, Chen Qian, Bao Wang, “Efficient and Re-liable Overlay Networks for Decentralized Federated Learning”, SIAM Journal on Applied Mathematics, 82 (4), 1558-1586. (2022).
14. Lisa Maria Kreusser, Stanley J. Osher, and Bao Wang, “A Deterministic Approach to Avoid Saddle Points”, European Journal of Applied Mathematics, 34 (4), 738-757, (2023).
15. Bao Wang, Hedi Xia, Tan Nguyen, Stanley Osher, “How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies”, Research in the Mathematical Sciences, 9 (3), 1-37. (2022).
16. Stanley Osher, Bao Wang, Penghang Yin, Xiyang Luo, Minh Pham, and Alex Lin, “Lapla-cian Smoothing Gradient Descent”, Research in the Mathematical Sciences, 9 (3), 1-26. (2022).

Profile Details

https://scholar.google.com/citations?user=_PTiDcoAAAAJ&hl=zh-CN&oi=sra