About

Sung Oh is a dedicated computer science student at Virginia Tech, where he maintains an impressive 3.9 GPA, including a perfect GPA in his major. 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 HCI 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 includes a wide range of programming languages such as Java, Python, and JavaScript/TypeScript, as well as cloud platforms like AWS and machine learning frameworks. In addition to his work at Fasoo, he built an AI-powered search engine for real-time financial data analysis at Fintellection, demonstrating his proficiency in leveraging machine learning and NLP to build impactful software solutions.