We present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh.

In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL. We present a novel SSL approach that trains the neural network on linear mixing of labeled and unlabeled data, at both the input and latent space in order to regularize different portions of the network.

We present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space.

Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this project, a metrically accurate, GPS-denied, indoor 3D static mapping system was developed using a moveable base coupled with three degree of freedom IMU.

Uncertainty are an inevitable part of a both real life events and computer simulation / modeling and their quantification is a very important and interesting research area.