About

Sung Oh is a dedicated computer science student at Virginia Tech, where he maintains an impressive 3.91 GPA. His research involvement spans multiple labs at Virginia Tech. At the Tech on the Trail Lab, he focuses on fine-tuning large language models for an Diary Study Platform to enhance content analysis and create intelligent templates. At the IDEEAS Lab, he applied NLP techniques to analyze social media data, discovering that 57% of public sentiments toward generative AI in computer science education were positive. This research led to a first-author paper presented at the IEEE FIE 2024 conference.

During his internship at Fasoo, Sung developed a cloud-based speech-to-text solution using WebSockets for real-time communication and Voice Activity Detection (VAD), which significantly improved transcription speed and accuracy. His work there demonstrated his ability to integrate advanced technologies into scalable solutions. These professional experiences, combined with his research, reflect Sung's capability to apply cutting-edge technologies to solve real-world problems.

Sung’s technical expertise encompasses programming languages such as Java, Python, and JavaScript/TypeScript, as well as cloud platforms like AWS and machine learning frameworks. Beyond his work at Fasoo, he developed an AI-powered search engine, Fintellection, for real-time financial data analysis. Sung is also a key contributor to VT Copilot, an AI-driven assistant designed to analyze student grade data and provide actionable insights. His work with VT Copilot highlights his ability to apply advanced analytics and machine learning to improve academic experiences for Virginia Tech students.