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I am graduated as Master in Brain and Machine Intelligence Lab (BML) at KAIST, advised by Sang Wan Lee. Before joining KAIST, I spent a semester taking a recommender system course from Boostcamp held by Naver Connect. Also, I received B.S. degree on majoring in Electrical and Electronic Engineering, and minoring in Psychology from Yonsei University. During undegraduate, I had a chance to work as an undergraduate research assistant in Computational Clinical Science and advised by Woo-Young Ahn. I was also advised by Sang Hoon Han for leading the undergraduate study club CogSci::IN.
CV / Github / Email / 전공요약발표자료 Skills & Tools
English Proficiency
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I'm interested in building AI systems that mimic and analyze human cognition, with the belief that this framework can be scaled to interpret complex real-world phenomena. To this end, I focus on designing deep learning models that learn causal and hierarchical structures of the environment, much like humans do. Additionally, I design novel experiments to study human cognition, gaining insights that inform and enhance AI development. |
Event Unit Prediction Research (23.07~24.12)
Human Experiment Platform: Built a Unity WebGL-based interactive experiment platform to collect human prediction data. Integrated Google Firebase for real-time data storage and analysis. Designed tasks where participants predict events in a physics-based environment (Angry Birds). Check online experiment link to conduct it by yourself! 
Hierarchical Video Prediction Model: A model that predicts future frames in a video by learning hierarchical structures. Designed a temporal abastraction model that incorporates causal inference & event-based representation. Demonstrated AI’s ability to simulate human-like event anticipation. |
Stable Predictive Coding Network Research (24.03~)
SPCN: Investigated stability issues in Predictive Coding Networks (PCN) and proposed a novel Stable Predictive Coding Network (SPCN). Conducted experiments to analyze instability factors in PCN, identifying key failure points. Designed a new architecture that stabilizes learning in PCN (SPCN), and showed improved performance in various tasks. Code is currently private, but will be released after the paper is accepted. |
Jo, Y., Lee, S. W. (2025). Designing a Platform and Algorithm for Event-Cognitive Unit Prediction Experiments. Patent pending. Ha, M., Sung, Y., Jo, Y. (2025). Towards Stable Learning in Predictive Coding Networks. International Joint Conference on Artificial Intelligence (IJCAI), under second review. |
Github Repository Recommendation Project (22.05~22.06)
Github Repository Recommendation Service: Built a personalized recommendation engine for GitHub repositories. Users struggle to find relevant repositories due to GitHub's vast dataset. Developed a Node.js-based REST API using MongoDB and machine learning ranking algorithms. Mitigated cold-start issues with hybrid recommendation models. |
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Source code from jonbarron |