Speakers

• Xiaowu Dai

  UC-Berkeley - Another Look at Statistical Calibration: A Non-Asymptotic Theory and Prediction-Oriented Optimality
• Qi Duan

  SenseTime - AI In Healthcare: Progress and Problem
• Shenghua Gao

  ShanghaiTech University - Anomaly Detection in Videos - from Feature Reconstruction to Future Prediction
• Ling Guo

  Shanghai Normal University - Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
• Lijian Jiang

  Tongji University - Adaptive Gaussian mixture model based on implicit sampling for Bayesian inverse problems
• Lei Li

  Shanghai Jiao Tong University - On validity of diffusion approximations for Stochastic Gradient Descent
• Qifeng Liao

  ShanghaiTech University - A Domain Decomposition Approach for Uncertainty Analysis
• Guang Lin

  Purdue University - Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media
• Liu Liu

  University of Texas at Austin - A bi-fidelity method for the multiscale Boltzmann and related kinetic equations with random parameters
• Zuoqiang Shi

  Tsinghua University - PDE-based Methods for Interpolation on High Dimensional Point Cloud
• Ruiwen Shu

  University of Maryland, College Park - A study of hyperbolicity of kinetic stochastic Galerkin system for the isentropic Euler equations with uncertainty
• Hao Wu

  Tsinghua University - Unbalanced Optimal Transport in Machine Learning
• Hao Wu

  Tongji University - Variational approach for learning Markov processes from time series data
• Zhiqin Xu

  New York University Abu Dhabi - Frequency Principle in Deep Neural Networks
• Haizhao Yang

  National University of Singapore - Approximation theory and regularization for deep learning
• Sixin Zhang

  Peking University - Wavelet Phase Harmonic Covariance Models of Stationary Processes
• Zhiwen Zhang

  The University of Hong Kong - A new data-driven method for multiscale elliptic PDEs with high-dimensional random coefficients
• Xiaoqun Zhang

  Shanghai Jiao Tong University - Data driven image reconstruction: Nonlocal Bayesian inversion and Deep Learning splitting approach
• Tao Zhou

  Chinese Academy of Sciences - Adaptive multi-fidelity surrogate modeling for Bayesian inference in inverse problems
• Yuhua Zhu

  Stanford University - Towards the theoretical understanding of large batch training in stochastic gradient descent