Publications

2021

Qualifier: Heuristic Swarm optimization and their application in Generative Adversarial Neural Networks.

“The purpose of the qualifying exam is to demonstrate the ability to read, analyze, synthesize, and communicate current research results. Qualifying exams are given twice a year, approximately mid-September and mid-February. Ph.D. students should take the qualifying exam at the beginning of their second year. Ph.D. students should start interacting with their initial advisor as soon as possible—preferably early in the fall semester—to set up a plan for starting research that will lead to success in the qualifying exam. Students must pass the qualifying exam by the end of their second year. A qualifying exam is based on a small number (3-5) of research articles selected in consultation with the student’s advisor. The candidate prepares a 15-20 page synthesis/discussion of this material.”

Qualifier presentation:  A panel of three faculty are selected by the Department for a 20-40 minute oral presentation on their qualifying paper

A presentation of my summer work at the National Renewable Energy Lab (NREL) for the Practice & Experience in Advanced Research Computing (PEARC) conference.

Link: <Click here>

2020

Due to the COVID 19 pandemic the conference has transitioned to be virtual. The presentation can be found here: Presentation

2019

Particle Swarm Optimizer Development Tools: This toolkit can be used to implement user-defined PSO analysis and visualization of a cost function. Currently, we only have a Gravitational Search Algorithm (GSA) developed and implemented. I hope to include other PSO techniques over time.

Abstract: Multi-physics codes for the simulation of flyer plate experiments have many parameters that directly affect a simulation’s fit to data. In particular, the Preston Tonks Wallace (PTW) strength model consists of eleven parameters and functions as a strength sub-model in simulations of material impact experiments. Using expert judgment and a Particle Swarm Optimizer (PSO) we traverse the parameter space of the PTW model in order to optimize individual parameters in the model while logging and interpolating the overall parameter space. We developed a Gravitational Search Algorithm that implements Newtonian physics to allow for the minimization of our cost function in the parameter space. In this talk, we will discuss the overall implementation, development, and results of this project.

 

Link: Beryllium Strength Model Parameter study using a Particle Swarm Optimizer

2018

Pyaesar: Named after Caesar for running a highly parallel distributed army to conquer the known world at the time this API was used for embarrassingly parallel data mapping and processing across nodes.

2017

Abstract: The study of morphological signatures of nuclear materials for forensic analysis has been a growing field for the past few years. With the development of the MAMA (Morphological Analysis of Materials) software, the field can be looked at under a new light. This software segments a given microscope image of a material sample and allows for specified quantification of the image. Therefore, MAMA will allow a more quantifiable analysis of data in nuclear forensics in support of national security. In this project, I am working on an extension of the software that will allow individuals to create their own plugin that can be implemented into the main software. This is a beneficial tool for the software because it opens up an interactive user base and builds a community that will allow for the exponential growth of options for the software as it progresses further. The development of the plugin is a necessity to allow for individual scientists who need to quantify morphology in their image set using an algorithm that they developed and cannot wait for it to be implemented in the next version of MAMA. While developing the plugin it was clear that a new format, design, and outline of the current documentation had to be reevaluated into a more modern user-friendly walkthrough.

Link: Nuclear Material Particle Analysis Developing a Plugin for the MAMA software