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SEMINARS

Weekly laboratory research seminars on research project development and critical review of literature in the interface of machine learning, deep learning and scientific computing.

SCIENTIFIC COMPUTING and

ARTIFICIAL INTELLIGENCE

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Boltzmann Machine; Undirected Graphical Models; Generative Models in Geology

Undirected Graphical Models; Deep Learning for Fluid Mechanics

Approximate Inference; Convolutional Neural Networks; Stein's Method

Parallel approaches for the Bayesian Gaussian process latent variable model; Regularization in optim

Simulation models for groundwater flow modeling; Cluster Expansion and Introduction to Alloy Theoret

UQ in Groundwater Modeling, State Space Models

A Review of Markov and hidden Markov Model, Regularization in Deep Learning

Implementation of Neural Nets in PyTorch

Deep Feedforward Networks, Bayesian Gaussian Process Latent Variable Model

Implementation of Neural networks using PyTorch

Sparse Gaussian Processes

Expectation Propagation, Model uncertainty in RANS simulation, Variational Auto-Encoders