Sparse covariance estimation in heterogeneous samples

Standard Gaussian graphical models (GGMs) implicitly assume that the conditional independence among variables is common to all observations in the sample. However, in practice, observations are usually collected form heterogeneous populations where such assumption is not satisfied, leading in turn to nonlinear relationships among variables. To tackle these problems we explore mixtures of GGMs; in particular, we consider both infinite mixture models of GGMs and infinite hidden Markov models with GGM emission distributions. Such models allow us to divide a heterogeneous population into homogenous groups, with each cluster having its own conditional independence structure. The main advantage of considering infinite mixtures is that they allow us easily to estimate the number of number of subpopulations in the sample. As an illustration, we study the trends in exchange rate fluctuations in the pre-Euro era. This example demonstrates that the models are very flexible while providing extremely interesting interesting insights into real-life applications

Energy Consumption and Routing Model for First Responder Vehicles

The ongoing research and prototyping of electric vehicles (EVs) offers numerous opportunities to investigate their performance in various service contexts. As EVs are integrated into society, the reliable prediction of fuel consumption and routing time becomes particularly important in emergency response services. This project develops a preliminary stochastic model that can route and predict the energy consumption and travel time for hypothetical emergency vehicles operating on an electric battery cell. Using a Monte-Carlo framework, we constructed a routing model designed to minimize travel time and resource consumption under various simulated conditions. In doing so, we establish the foundation for balancing the demands of time and energy in a relatively unexplored context and determined the impact of elevation, distance, time, and other factors on energy consumption for these large vehicle types. My model computes likely travel times, power consumption, and best-suited resulting route for emergency vehicles from the Orange County Fire Station to four locations on the University of Central Florida campus: Millican Hall, Lake Claire, Jay Bergman Field, and the Creative School for Children. My model provides consistent results that are comparable to real-world travel times recorded by the Orange County Fire Station. Future work will include more robust and accurate iterations of the model that could ultimately be a useful tool for both first responders as well as other EV services that require efficient resource allocation and time forecasting in routing

Coarse-grained Multiresolution Structures for Mobile Exploration of Gigantic Surface Models

We discuss our experience in creating scalable systems for distributing and rendering gigantic 3D surfaces on web environments and common handheld devices. Our methods are based on compressed streamable coarse-grained multiresolution structures. By combining CPU and GPU compression technology with our multiresolution data representation, we are able to incrementally transfer, locally store and render with unprecedented performance extremely detailed 3D mesh models on WebGL-enabled browsers, as well as on hardware-constrained mobile devices