YoungJae Park

Ph.D. Candidate 

Gwangju Institute of Science and Technology (GIST)

youngjae.park [at] gm.gist.ac.kr


CV | Google Scholar | LinkedIn

About me

I am a Ph.D. student advised by Prof. Hae-Gon Jeon. I obtained my B.S. from Konkuk University (2020). I have been fortunate to collaborate with SI Analytics (2023).  

My research interests are focused on developing advanced methodologies in machine learning, specifically for trajectory prediction, improving domain adaptation techniques in computer vision, and applying artificial intelligence to promote social good through crime prediction and typhoon forecasting.  

Ultimately, my long-term goal is to address and solve various social issues using artificial intelligence.  

Publications

What Makes Deviant Places?

Jin-Hwi Park*, Young-Jae Park*, Ilyung Cheong, Junoh Lee, Young Eun Huh and Hae-Gon Jeon (*equally contributed)

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Accepted  [Impact Factor: 23.6]

[Paper] 

SingularTrajectory: Universal Trajectory Predictor using Diffusion Model

Inhwan Bae, Young-Jae Park and Hae-Gon Jeon

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2024

[Paper] [Project page] [Source code and Dataset]

Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data Spotlight 

Young-Jae Park*, Minseok Seo*, Doyi Kim, Hyeri Kim, Sanghoon Choi, Beomkyu Choi, Jeongwon Ryu, Sohee Son, Hae-Gon Jeon and Yeji Choi (*equally contributed)

The Twelfth International Conference on Learning Representations (ICLR), May 2024

[Paper] [Source code and Dataset]

High-fidelity 3D Human Digitization from Single 2K Resolution Images Highlight 

Sang-Hun Han, Min-Gyu Park, Ju Hong Yoon, Ju-Mi Kang, Young-Jae Park and Hae-Gon Jeon 

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2023

- Silver Prize, 29th HumanTech Paper Award, Samsung Electronics Co., Ltd.

- Qualcomm Innovation Fellowship Korea 2023

[Paper] [Project page] [Source code and Dataset]

DevianceNet: Learning to Predict Deviance from A Large-scale Geo-tagged Dataset

Jin-Hwi Park*, Young-Jae Park*, Junoh Lee and Hae-Gon Jeon (*equally contributed)

The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), Feb 2022

[Paper] [Project page] [Source code and Dataset]

A Deep Learning-based Time Series Model with Missing Value Handling Techniques to Predict Various Types of Liquid Cargo Traffic

Sunghoon Lim, SunJun Kim, Young-Jae Park and Nahyun Kwon

Expert Systems with Applications, Dec 2021  [Impact Factor: 8.665]

[Paper]

Awards & Honors