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Young Researcher Workshop on Uncertainty Quantification and Machine Learning

Day 1, 5 June, Wednesday

Time Speaker Title
08:30 - 09:00 Registration
09:00 - 09:30 Guang Lin Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media
09:30 - 10:00 Zuoqiang Shi PDE-based Methods for Interpolation on High Dimensional Point Cloud
10:00 - 10:30 Lijian Jiang Adaptive Gaussian mixture model based on implicit sampling for Bayesian inverse problems
10:30 - 11:00 Group photo & Coffee Break
11:00 - 11:30 Tao Zhou Adaptive multi-fidelity surrogate modeling for Bayesian inference in inverse problems
11:30 - 12:00 Lei Li On validity of diffusion approximations for Stochastic Gradient Descent
12:15 - 13:45 Lunch
14:00 - 14:30 Xiaoqun Zhang Data driven image reconstruction: Nonlocal Bayesian inversion and Deep Learning splitting approach
14:30 - 15:00 Hao Wu Unbalanced Optimal Transport in Machine Learning
15:00 - 15:30 Zhiwen Zhang A new data-driven method for multiscale elliptic PDEs with high-dimensional random coefficients
15:30 - 16:00 Coffee Break
16:00 - 16:30 Yuhua Zhu Towards the theoretical understanding of large batch training in stochastic gradient descent
16:30 - 17:00 Xiaowu Dai Another Look at Statistical Calibration: A Non-Asymptotic Theory and Prediction-Oriented Optimality

Day 2, 6 June, Thursday

Time Speaker Title
09:00 - 09:30 Haizhao Yang Approximation theory and regularization for deep learning
09:30 - 10:00 Ling Guo Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
10:00 - 10:30 Sixin Zhang Wavelet Phase Harmonic Covariance Models of Stationary Processes
10:30 - 11:00 Coffee Break
11:00 - 11:30 Ruiwen Shu A study of hyperbolicity of kinetic stochastic Galerkin system for the isentropic Euler equations with uncertainty
11:30 - 12:00 Liu Liu A bi-fidelity method for the multiscale Boltzmann and related kinetic equations with random parameters
12:15 - 13:45 Lunch
14:00 - 14:30 Qi Duan AI In Healthcare: Progress and Problem
14:30 - 15:00 Shenghua Gao Anomaly Detection in Videos - from Feature Reconstruction to Future Prediction
15:00 - 15:30 Zhiqin Xu Frequency Principle in Deep Neural Networks
15:30 - 16:00 Coffee Break
16:00 - 16:30 Qifeng Liao A Domain Decomposition Approach for Uncertainty Analysis
16:30 - 17:00 Hao Wu Variational approach for learning Markov processes from time series data