Chae Young Lee
About Chae Young Lee
Chae Young Lee is a Research Assistant at Yale University, with a background in machine learning and a Bachelor of Science in Electrical Engineering and Computer Science. Lee has authored a paper presented at the ICDAR Workshop and has held various internships in prominent companies, including Apple and NAVER Corp.
Current Role at Yale University
Chae Young Lee currently serves as a Research Assistant at Yale University, a position held since 2021. In this role, Lee contributes to various research projects within the university's academic framework. Additionally, Lee has taken on the responsibilities of a Teaching Assistant at Yale University since 2021, further supporting the educational mission of the institution.
Previous Experience at Yale University
Prior to the current roles, Chae Young Lee worked at Yale University in several capacities. Lee was a Teaching Assistant for three months in 2020 and contributed to the Yale Center for Research Computing for five months from 2019 to 2020. Lee also worked at the Peabody Museum of Natural History for six months during the same period, gaining diverse experience within the university.
Internship Experience at Apple and NAVER Corp
Chae Young Lee gained valuable industry experience through internships at prominent technology companies. Lee interned at Apple for two months in 2020 and again for two months in 2021, both in Cupertino, California. Prior to that, Lee worked as a Machine Learning Intern at NAVER Corp for six months in 2019 in Seongnam, Gyeonggi, South Korea.
Educational Background
Chae Young Lee completed a Bachelor of Science in Electrical Engineering and Computer Science at Yale University from 2019 to 2023. Prior to this, Lee attended Hankuk Academy of Foreign Studies, where a High School Diploma was achieved from 2016 to 2019. This educational background laid the foundation for Lee's expertise in machine learning and related fields.
Research Contributions and Interests
Chae Young Lee has made significant contributions to the field of machine learning, including authoring a paper titled 'TedEval' presented at the ICDAR Workshop in 2019. Lee developed 'Conditional WaveGAN', which was featured at a NeurIPS Workshop in 2018. Lee maintains a GitHub repository showcasing projects and contributions, reflecting a strong interest in Text-to-Speech (TTS) technology, VLSI, and Embedded Systems.