Masters Research Project
Project Title: Measuring Career Preparedness with Topic Modeling
This self directed research was performed within the framework of the Georgia Tech Educational Technology research course. It explored the relationship between post-secondary education and the expectations of the workforce. Specifically it applied Topic Modeling to university course descriptions and industry job listings.
Senior Design Research Project
Project Title: What Are We Teaching? Automated Evaluation of CS Curricula Content Using Topic Modeling
This research was performed under the supervision of Huzefa Rangwala at George Mason University. We explored the application of Topic Modeling to textual university curriculum data, specifically course descriptions. Automatically extracting content from this corpus allows for larger-scale analysis of university curricula than would be possible with manual inspection.
In addition to extracting topics from course documents, I independently developed an easy-to-use, web-based visualization platform to display the results of information extraction and analysis. The visualization tool is built with Flask.
UA REU Empirical Software Engineering
Project Title: Usability and Suitability Survey of Features in Visual IDEs for Non-Programmers
Summer 2014 I was engaged as an undergraduate researcher with the University of Alabama and the National Science Foundation conducting research into software engineering theory and application with the UA REU ESE program.
The project involved the systematic empirical study of user interfaces in visual language integrated development environments (IDEs). Along with Dr. Eugene Syriani we analyzed a set of visual language IDEs and developed formal metrics for comparison. The goal of this research was to understand the important positive and negative features of visual language IDEs in a formal setting.
Project Title: Large-scale Clustering for Big Data Analytics: A MapReduce Implementation of Hierarchical Affinity Propagation
Summer 2013 I was engaged as an undergraduate researcher with the Florida Institute of Technology and the National Science Foundation conducting research into machine learning in theory and application with the Amalthea REU program.
The project involved architecting and implementing a Big Data scale data-mining framework. The application was built on existing free and open source software frameworks including Apache Hadoop for MapReduce and distributed filesystem implementations, Apache Mahout libraries for mathematical support and algorithm benchmarking, and Apache Hive for data warehousing and post-processing data manipulation.
Specifically, we parallelized the exemplar-based clustering algorithm Hierarchical Affinity Propagation using a MapReduce framework. Preliminary results of running the distributed algorithm on AWS Elastic MapReduce (EMR) service indicate superior performance as compared to competitive algorithms from the Apache Mahout libraries.