PH.D. STUdent

Jin-Young Jung, Integrated Ph.D. Course

Research interest

Jin-Young Jung is a Ph.D. student at Kyungpook National University, advised by Prof. Hyun-Chul Kim. His research focuses on developing real-time adaptive learning systems by establishing mathematical connections between biological principles and modern deep learning architectures like Transformers (ViT, Perceiver). Key research areas include:

• The core problem: Online Continual Learning & Neural Dynamics

• Theoretical analysis: Catastrophic Forgetting & Gradient Confusion & Sharpness

• Proposed approach: Sparse Activations and Updates

• Learning Algorithm: Iterative Attention and Predictive Coding

Education

• Mar. 2022 - Present:  Integrated M.S. and Ph.D., Dept. of Artificial Intelligence, Kyungpook National University, Daegu, Republic of Korea 

• Mar. 2016 - Feb. 2020:  B.S., Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea

Work experience

• Oct. 2021 - Mar. 2022 : Collaborative Research Intern, Transaction Analysis, OKX (via Kyunpook National University)

Award

• Best paper award, Brain Engineering Society of Korea (BESK), 2022 Summer Workshop

• Best paper award, KNU-EE-Research Congress (KNU-EERC), 2025

• Best poster award, Brain Engineering Society of Korea (BESK), 2022 Summer Workshop

• Best poster award, Korean Society for Human Brain Mapping (KHBM) 2022 Fall Workshop

Tutorial, invited talk, and seminar

• Jul. 10, 2025,  Unified Gradient Conflict Control for Robust, Brain-Like Learning under Shift, Brain Engineering Society of Korea (BESK) 2025 Summer Workshop (Oral presentation, Best paper award)

• Jul. 1, 2025, Unified Gradient Conflict Control: Architectural Sparsity and Gradient Regularization for Non-I.I.D. Adaptation, KNU-EE Research Congress (KNU-EERC) 2025 (Oral presentation, Best paper award)

• Sep. 27, 2024, Sparceiver: Leveraging Dynamic Sparsity in Transformers for Better Shift Adaptation and Interpretability, KPK Workshop, Daegu, Republic of Korea

• Sep. 26, 2024, Sparceiver: A Sparse Transformer for Mitigating Catastrophic Forgetting through Reducing Gradient Confusion, Brain Image Research Seminar (online)

• May 17, 2024,  Sparceiver: Overcoming Gradient Confusion with Dynamic Attention Threshold in Transformers, Korean Society for Industrial Applied Mathematics  (KSIAM) Spring Conference, Daegu, Republic of Korea

• June 29, 2023, Exploring Brain-plausibility and Modifications for the Perceiver, Brain Image Research Seminar (online)