Youngjo Min

I am a Ph.D. student in Computer Science at Duke University, advised by Professors David Carlson and Carlo Tomasi. My research focuses on 3D and 4D computer vision, with an emphasis on reconstructing and understanding dynamic scenes from multimodal data.

I study 3D human and animal reconstruction and investigate the relationship between neural signals and behavior through multimodal learning. I am also interested in autonomous systems, where I explore 4D scene reconstruction and sensor fusion across modalities such as cameras, LiDAR, and radar.

More broadly, I aim to develop unified representations that connect perception, behavior, and world modeling across domains.

AI/ML Research Intern

05/2026 – 08/2026          General Motors Autonomous Driving Team (Cruise)

• Mentors: Tung Phan Minh, Eric M. Wolff

• Developing 4D Gaussian Splatting models for dynamic scene reconstruction in autonomous driving environments.

• Researching multimodal sensor fusion across cameras, LiDAR, and radar for robust and scalable world modeling.

Graduate Research Assistant 

09/2023 - 05/2027 (expected)          Duke University           

• Developing methods to reconstruct 3D shape and pose of humans and animals using 3D Gaussian Splatting. 

• Exploring relations between brain signals and 3D animal behavior.

Researcher

09/2022 - 08/2023          Seoul National University

• Developed unsupervised open-set domain adaptation model using a vision-language model

Undergraduate Researcher

08/2020 - 08/2022          Korea University

• Developed a semi-supervised classifcation framework using a learnable confidence estimator and a confidence-aware consistency regularization.

• Created an implicit neural representation-based style transfer model that can handle a variety of flexible image translation tasks.

• Developed a framework for 3D human recovery from a single image by combining meta-learning with test-time optimization.

• Devised a semantic matching model that jointly optimizes feature extraction and cost aggregation modules

Air Force Sergeant

02/2017 - 02/2019          Republic of Korea Air Force 

• Participated in multiple Korea–U.S. joint military exercises and operations conducted with U.S. Forces Korea and the U.S. 7th Air Force.

Pose Splatter: A 3D Gaussian Splatting Model for Quantifying Animal Pose and Appearance

Jack Goffinet*, Youngjo Min*, Carlo Tomasi, and David Carlson 

(* Equal Contribution) 

Neural Information Processing Systems (NeurIPS), 2025.

[Paper]  [Project]

ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization

Jiwon Kim*, Youngjo Min*, Daehwan Kim*, Gyuseong Lee, Junyoung Seo, Kwangrok Ryu, and Seungryong Kim 

(* Equal Contribution)

European Conference on Computer Vision (ECCV), 2022.

[Paper]  [Project]

Controllable Style Transfer via Test-time Training of Implicit Neural Representation 

Sunwoo Kim*, Youngjo Min*, Younghun Jung*, and Seungryong Kim 

(* Equal Contribution) 

Pattern Recognition, 2024.

[Paper]  [Project]

UOTA: Unsupervised Open-Set Task Adaptation Using a Vision-Language Foundation Model

Youngjo Min*, Kwangrok Ryu*, Bumsoo Kim, and Taesup Kim 

(* Equal Contribution)

ICML 2023 Workshop on Efficient Systems for Foundation Models.

[Paper]

Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence

Jiwon Kim, Youngjo Min, Mira Kim, and Seungryong Kim 

IEEE International Conference on Acoustics, Speech, and Signal Processing

(ICASSP), 2022.

[Paper] 

Meta-Learned Initialization for 3D Human Recovery

Mira Kim, Youngjo Min, Jiwon Kim, and Seungryong Kim

IEEE International Conference on Image Processing (ICIP), 2022.

[Paper] 

Semi-Supervised Learning with Confidence

Youngjo Min, Kwangrok Ryoo, Jiwon Kim, Junyoung Seo, Kyusung Lee, Seungryong Kim

IEIE Conference (South korean domestic conference), 2022.

[Paper] 

Confidence-guided Semantic Correspondence

Youngjo Min, Kwangrok Ryoo, Jiwon Kim, Suhyung Choi, Kyusun Cho, Minhyek Jeon, Seungryong Kim

IEIE Conference (South korean domestic conference), 2022.

[Paper]