This seminar aims to introduce cutting-edge research of Data Science, especially, the application of Data Science in scientific problems and the "science" part of Data Science, such as understanding deep learning. Welcome to contact Zhiqin Xu (xuzhiqin@sjtu.edu.cn).
Partial Differential Equation Principled Trustworthy Deep Learning
Bao Wang, University of California, Los Angeles
2020.04.23, 12:30-13:30

The Tradeoffs and Layered Architecture in Brain
Quanying Liu, Southern University of Science and Technology
2020.04.08, 14:00-15:00

Finite Elements and Deep Neural Networks
Juncai He, Penn State University
2020.03.27, 10:00-11:00

A-Priori Estimates of Population Risks for Neural Networks Models
Chao Ma, Princeton University
2020.03.19, 10:00-11:00

Structure Exploration for 3D Reconstruction
Shenghua Gao, ShanghaiTech University
2019.12.18, 12:20-13:50

On the Understanding of Vulnerability of Deep Learning and Beyond
Yisen Wang, Department of Computer Science and Engineering, Shanghai Jiao Tong University
2019.12.11, 15:00-16:00

"Kernel Mode Decomposition and Programmable/Interpretable Regression Networks" by Owhadi Et. Al.
Lei Zhang, Institute of Natural Sciences, Shanghai Jiao Tong University
2019.12.04, 12:20-13:50

Physics-Informed Neural Networks for Solving Forward and Inverse Stochastic Problems
Ling Guo, Shanghai Normal University
2019.11.27, 12:20-13:50

Int Deep: A Deep Learning Initialized Iterative Method for Nonlinear Problems
Jianguo Huang, School of Mathematical Sciences, and LSC-MOE, Shanghai Jiao Tong University
2019.11.20, 12:20-13:50

Theoretical Understanding of Stochastic Gradient Descent in Deep Learning
Zhanxing Zhu, Peking University
2019.11.13, 12:20-13:50

Optimization and Generalization Property of Two-Layer Neural Network under Gradient Descent Dynamics
Chengchao Zhao, Beijing Computational Science Research Center
2019.10.23, 12:20-13:50

Deep Neural Networks and Finite Element Methods
Jihong Wang, Beijing Computational Science Research Center
2019.10.16, 12:20-13:50