Lijing Wang's Projects
ClusterJob: An automated system for painless and reproducible massive computational experiments
Advanced Topics in Scientific Computing with Julia
Code examples in pyTorch and Tensorflow for CS230
Stochastic geological surface modeling
Data Science for the Geosciences
Deep Residual Learning for Image Recognition
An R implementation of the DGSA method
This is a light version of DGSA, written in Python
Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
A tutorial session on convolutional neural network for Stanford Data Science for Social Good program
An R package that implements the methods of geostatistics for functional data
A Python package to create, run, and post-process MODFLOW-based models.
An R package that implements methods for growing regression trees with functional and multivariate outputs
Data science for geoscience
Tutorials and resources for GS 260 Uncertainty Quantification in Subsurface Systems
Open-source Python package for Hierarchical Bayesian inversion of global variables and large-scale spatial fields.
Introduction to Spatial Data Analysis, Data Science Blog @ Stanford Data Science Institute
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
Companion code for Scheidt, C, Li, L, and Caers, J. K. Quantifying Uncertainty in Subsurface Systems, John Wiley & Sons, 2017.
scikit-fmm is a Python extension module which implements the fast marching method.
Texture synthesis in Torch
This is the first version of the Tree-based Direct Sampling (TDS), with 2D Antactica Topography modeling case as example.