• Myinterdisciplinary expertise is at the intersection of machine learning, earth science and flow physics. I interface current ML/DL tools with domain-specific tools to solve complex problems in dynamical systems.
• I aspire to make ML/DL more accessible and explainable to practitioners.

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1. cnn-regressioncnn-regressionPublic

   A simple guide to a vanilla CNN for regression, potentially useful for engineering applications.

   Jupyter Notebook 44 11

2. timeseries-rnntimeseries-rnnPublic

   Time-series forecasting with 1D Conv model, RNN (LSTM) model and Transformer model. Comparison of long-term and short-term forecasts using synthetic timeseries. Sequence-to-sequence formulation.

   Jupyter Notebook 30 8

3. latent-space-data-assimilation-lsdalatent-space-data-assimilation-lsdaPublic

   Ensemble-based history matching method with latent-space proxy model for nonlinear forward model and non-Gaussian models.

   Jupyter Notebook 12 5

4. gansgansPublic

   Keras implementation of dcgan, wgan and wgan-gp with digit-MNIST dataset for tutorials.

   Python

5. dimensionality-reduction-classicdimensionality-reduction-classicPublic

   Tutorials on classic dimensionality reduction techniques using tailored bases (SVD) and generic bases (Fourier and Wavelets).

   Jupyter Notebook 3 2

6. cnn-classifiercnn-classifierPublic

   A simple guide to a vanilla CNN for classification and transfer learning

   Jupyter Notebook 5 1