I am Juhwan Park, specializing in computer vision and deep learning. My research primarily focuses on video-based monocular depth estimation and temporal consistency, balancing theoretical rigor with practical implementation. I've optimized various CNN and Transformer architectures, emphasizing efficient GPU resource utilization. Experienced in handling diverse datasets, I consistently seek methods to enhance model performance. My goal is to pursue graduate studies, contributing deeply to solving practical challenges through advanced research.
- 2021
- Technology-Based Entrepreneurship Capstone Design Competition - First Prize
- '์ฌ์ฉ์ ๊ฐ์ธ ์๋ฅ ์ฌ์ง์ ์ด์ฉํ ํจ์ ์ฝ๋ ์ถ์ฒ ์์คํ ๋ฐ ๊ทธ ๋ฐฉ๋ฒ'์ผ๋ก ํนํ ์ถ์ ๋ฐ ๋ฑ๋ก( 10-2021-0181723)
- 2022
- 2022 OSAM Hackathon - Second Prize(์ ๋ณดํต์ ์ฐ์ ์งํฅ์์ฅ)
- 2023
- 2023 SW๊ฐ๋ฐ ์ง๋ฌด๋ถํธ์บ ํ ๋ฒก์๋ ๊ณผ์ ์๋ฃ (COMENTO ์ฃผ๊ด)
- 2024
- DSAC (Data Scientist Academy & Certificate) - M7 SEAT 1, 2
- ICONS lab Intern
- joining at PyTorch Korea, open-source project
sponsored by HYUNDAI MOTOR CHUNG MONG-KOO FOUNDATION


