Sociotechnological and geospatial processes exhibit time varying structure that make insight discovery challenging. This project proposes a new statistical model for such systems, modeled as dynamic networks, to address this challenge. It assumes that vertices fall into one of k types and that the probability of edge formation at a particular time depends on the types of the incident nodes and the current time. The time dependencies are driven by unique seasonal processes, which many systems exhibit (e.g., predictable spikes in geospatial or web traffic each day). The project defines the model as a generative process and an inference procedure to recover the seasonal processes from data when they are unknown.

So far we have one workshop publication [1] with more to come!

In the past summer of 2017, I worked as an intern at the National Aeronautics and Space Administration (NASA) Glenn Research Center with Debra Goodenow and Jerry Myers. I worked in the human research program to develop a dynamic probabilistic risk assessment tool to quantify the level of risk astronauts face from medical conditions in various mission scenarios.

We expect to publish at the NASA Human Research Program Investigators' Workshop January 2018!

The Iterative Closest Point algorithm is a widely used approach to aligning the geometry between two 3 dimensional objects. The capability of aligning two geometries in real time on low-cost hardware will enable the creation of new applications in Computer Vision and Graphics such as automated aerial refueling between a tanker and jet. This work presents an accelerated alignment variant which utilizes parallelization on a Graphics Processing Unit (GPU) of multiple kNN approaches augmented with a novel Delaunay Traversal to achieve real time estimates.

This work resulted in two conference publications [2] [3].

Angle of Arrival (AOA) measurements can be created from any sensor platform with any sort of optical sensor, location and attitude knowledge to track passive objects. Previous work has created a non-linear optimization (NLO) method for calculating the most likely estimate from AOA measurements. Two new modifications to the NLO algorithm are created and shown to correct AOA measurement errors by estimating the inherent bias and time-drift in the Inertial Measurement Unit (IMU) of the AOA sensing platform.

This work resulted in a conference publication [4].