Quinn Hubbarth
About Quinn Hubbarth
Quinn Hubbarth is an iOS/Swift Software Developer currently employed at Brooksource and Duke Energy Corporation since 2022. He holds a Bachelor's degree in Computer Science from Clemson University, where he also worked as a Machine Learning Researcher and participated in various programming projects.
Work at Brooksource
Quinn Hubbarth has been employed at Brooksource as an iOS/Swift Software Developer since 2022. In this role, Quinn focuses on developing applications for the iOS platform using Swift programming language. The position allows for collaboration with various teams to enhance mobile application functionality and user experience.
Current Role at Duke Energy Corporation
In addition to working at Brooksource, Quinn Hubbarth also serves as an iOS/Swift Software Developer at Duke Energy Corporation since 2022. This remote position involves similar responsibilities, contributing to the development of mobile applications that support the company's operations and services.
Education and Expertise
Quinn Hubbarth completed a Bachelor's degree in Computer Science at Clemson University, finishing the program in just three years from 2018 to 2021. During this time, Quinn gained knowledge in various areas, including artificial intelligence, mobile app development, and algorithms. The education provided a strong foundation in programming and software development.
Previous Experience in Software Development
Prior to current roles, Quinn worked at LPL Financial as a Software Engineer from 2021 to 2022. In this position, Quinn focused on SQL and PL/SQL programming to manage a large-scale Oracle database. Additionally, Quinn interned at GE Power in 2020, where skills in AWS services and NoSQL database principles were developed.
Research and Publications
Quinn Hubbarth has experience in research, having worked as a Machine Learning Researcher at Clemson University. This role included leading a team on the IBM Watson in the Watt research project. Quinn contributed to the publication of multiple articles on machine learning methods, showcasing the research team's findings on their website.