The benefits of the GQP project experience and the tremendous learning opportunities afforded through these projects on both professional and personal levels are priceless for the students. Beyond these benefits, our students secure amazing achievements and successes as part of their GQP experiences that range from filing a patent application as inventors (sponsor UTC), publishing the results of their projects in professional venues, contributing to the bottom line of the sponsoring company, and winning the first GQP project prize at the GRIE graduate student research symposium, to securing a lucrative job offer from their company sponsors.
Further, we note that each student’s journey is personal. Yet all the students came out of the experience with a new skill set and experience to impart upon their future employers. Each with an individualized lens, they share a glimpse into just what life was like as they worked on a project, and where they hope to go from here.
GQP
Spring 2019
Project Sponsor
Andplus
Project
Our GQP team consisted of three members, two of which are still in their second semester of their master’s program—and one (i.e. me, I am in my final semester). They are pursuing this project as an independent study.
GQP
Spring 2019
Project Sponsor
National Fire Protection Association (NFPA)
Project
My GQP team had four members, all from the Data Science program. The team was a mix of Math-heavy as well as Computer Science-heavy students, which was beneficial in the project implementation.
GQP
Fall 2018
Project Sponsor
Findability Sciences
Project
Our team had five team members and we were looking to address a problem in which we would create automated libraries for acquisition, import, analysis, and cleaning data. This was important to reduce the time of data cleaning.
GQP
2019 Spring
Project Sponsor
Kronos
Project
We were a team of four students—all MS in Data Science. Our goal for the GQP was to analyze the knowledge base articles to determine what is covered, where is the duplication, and strategize how to improve the documentation. We were provided with the dataset; the corpus had about 19000+ documents for different articles. Our approach was to apply unsupervised learning on the data; the architecture of the model was to get the tf-idf and reduce the dimension by applying PCA and cluster the m in low dimension.
