- My interdisciplinary 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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University of Southern California
- Los Angeles, CA
Highlights
- Pro
Pinned Loading
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cnn-regression
cnn-regression PublicA simple guide to a vanilla CNN for regression, potentially useful for engineering applications.
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timeseries-rnn
timeseries-rnn PublicTime-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.
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latent-space-data-assimilation-lsda
latent-space-data-assimilation-lsda PublicEnsemble-based history matching method with latent-space proxy model for nonlinear forward model and non-Gaussian models.
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gans
gans PublicKeras implementation of dcgan, wgan and wgan-gp with digit-MNIST dataset for tutorials.
Python
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dimensionality-reduction-classic
dimensionality-reduction-classic PublicTutorials on classic dimensionality reduction techniques using tailored bases (SVD) and generic bases (Fourier and Wavelets).
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cnn-classifier
cnn-classifier PublicA simple guide to a vanilla CNN for classification and transfer learning
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