Jinyoon Kim
I received my Bachelor's degree in Computer Science from Penn State University in 2024 and am currently pursuing my Master's in Computer Science at the University of Virginia. My research interests lie in artificial intelligence, with a focus on the intersection of computer vision, large language models, and multimodal learning. I am particularly interested in developing architectures that integrate medical imaging and electronic health records with retrieval-augmented and knowledge-enhanced methods to improve reasoning and decision support in healthcare. My broader interests also include reinforcement learning for more human-like and trustworthy AI, as well as extending multimodal research to diverse data sources such as brain imaging, physiological signals, and surgical data. Previously, I have worked on medical image analysis, automated self-supervised learning systems, publishing results under the guidance of my mentors.
CV| GitHub | LinkedInPublications
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YOLO-SCSA: Enhanced YOLOv8 with Spatially Coordinated Shuffling Attention Mechanisms for Skin Cancer Detection
Jinyoon Kim, Tianjie Chen, Hien Nguyen, and Md Faisal Kabir. In Proceedings of IEEE International Conference on Machine Learning and Applications (ICMLA 2024), pp. 408-415, Dec 15, 2024.
[ paper | code ] -
Automated Image Segmentation Using Self-Iterative Training and Self-Supervised Learning with Uncertainty Scores
Jinyoon Kim, Tianjie Chen, and Md Faisal Kabir. In Book of Recent Advances in Deep Learning Applications: New Techniques and Practical Examples, Chapter 1, April 4, 2025.
[ paper | code | Book Weblink ] -
Automated Data Labeling for Object Detection via Iterative Instance Segmentation
Jinyoon Kim and Md Faisal Kabir. In Proceedings of IEEE International Conference on Machine Learning and Applications (ICMLA 2023), pp. 845-850, Dec 15, 2023.
[ paper | poster | code ]
Projects
Jinyoon Kim [ code ]
- Developing a text-guided 3D editing pipeline for Mip-NeRF 360 scenes using gsplat, InstructPix2Pix, GroundingDINO, and SAM.
- Focuses on precise object selection via SAM and iterative scene modification using constrained sequential diffusion painting.
- Aims to create an interactive system allowing users to select objects in 2D views, identify corresponding 3D Gaussians, and perform edits validated across multiple views.
Jinyoon Kim [ code ]
- Implementing and comparing multiple Reinforcement Learning methods (PPO, GRPO, RLOO, DPO) for fine-tuning Large Language Models.
- Utilizing the FinQA dataset to improve mathematical reasoning capabilities in financial question-answering tasks.
- Employs a modular, file-per-module design for clarity, reproducibility, and ease of verification throughout the SFT → RL → Eval pipeline.
Jinyoon Kim, Tianjie Chen, Hien Nguyen, and Md Faisal Kabir [ slides | project code | web app code ]
- Created an accessible and user-friendly web application for skin cancer detection using YOLOv8.
- Utilized combined ISIC datasets, implemented confounding factors removal, and incorporated interpretability techniques.
- Developed a PWA-based web application ensuring wide accessibility and transparent diagnostics through visual interpretability.
Jinyoon Kim, Aditya Kendre, et al. [ slides | code ]
- Built a face recognition system focused on high accuracy and effective feature extraction.
- Developed using a fine-tuned ResNet model and implemented a Top-k features algorithm.
- Successfully created a model capable of accurately classifying team members via face recognition.
Jinyoon Kim [ code | video ]
- Created a mobile application for detecting plant diseases using neural networks.
- Developed and fine-tuned MobileNet specifically for plant disease detection on mobile devices.
- The resulting application runs efficiently and accurately classifies plant disease images in a mobile environment.
Awards & Honors
- National Ackroyd Healthier Days Scholarship, 2024. Awarded for a research project focused on improving health environments for patients through a skin cancer detection application. This project was conducted in collaboration with the Ackroyd Family Foundation and the Penn State community.
- PennState Computer Science Department Undergraduate Student Award, 2023. Recognized by the Penn State Computer Science Department for my participation in the ICMLA 2023 conference, where I presented research on a self-supervised learning algorithm for an automated image segmentation system.
News
