Younghan Park

I am an undergraduate student majoring computer science at Yonsei University. My long-term research goal is to build machines that can perceive, understand, and use language in a human-like way. My current areas of interest are:

Multimodal Language Processing: How can we teach machines to understand the information that arises from the fusion of text and other modalities?
Conversational AI: How should contextual information be processed for human-like conversations with AI? How can machines use this information while generating response?

Email: younghanpark [at] yonsei [dot] ac [dot] kr
Curriculum Vitae / GitHub / LinkedIn

News

(Jul 2024) I am excited to share that I have been selected as a recipient of the Presidential Science Scholarship!

Education

Yonsei University

, Seoul, South Korea
B.S. in Computer Science, Mar 2020 - Present

The University of Texas at Austin

, Austin, Texas, United States
Exchange Student in Computer Science, Aug 2023 - May 2024

Research Experience

NLP and Computational Linguistics Group

, The University of Texas at Austin
Undergraduate Research Intern, Aug 2023 - Present
Advisors: Prof. Eunsol Choi, Prof. David Harwath

Multimodal AI Lab

, Yonsei University
Undergraduate Research Intern, Feb 2023 - Aug 2023
Advisor: Prof. Youngjae Yu

Honors and Awards (Selected)

Presidential Science Scholarship

, Korea Student Aid Foundation, Jul 2024 - Present
The highest honor an undergraduate STEM student in South Korea can receive - full-ride scholarship with an additional stipend provided each semester.

Projects (Selected)

Prior to research, I've also had the chance to participate in a lot of fun and interesting projects.

	MouthMouse , Apr 2020 - Sep 2022 project page / patent A novel, low-cost human interface device utilizing the oral cavity, enabling easier computer and mobile device usage for individuals with physical disabilities.
	CO2Gather , Jan 2022 - Feb 2022 project page / code An NLP software analyzes card logs to automatically calculate potential savings, fostering sustainable collaboration between consumers, businesses, and governments.
	MaGam , Jan 2021 - Feb 2021 project page / code A computer vision-based full-stack web application for highly accurate mask detection by addressing the challenge of limited data using both classical and modern features.