People

Yae Ji Kim, Ph.D.  (Post-doc)

Ph.D. 2024. Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST).
B.S. 2018. Psychology, The University of Utah.

Yae Ji received her Ph.D. from the Department of Bio and Brain Engineering (Program of Brain and Cognitive Engineering) at the Korea Advanced Institute of Science and Technology (KAIST) in Daejeon, Korea. Her research primarily focused on exploring the underlying mechanisms of neurodegenerative disorders in the human brain, particularly Parkinson’s disease, and discovering its biomarkers using multiple neuroimaging techniques, such as diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). 

She is currently interested in understanding the heterogeneity of brain activity in individuals across the lifespan. By examining these variations, she aims to predict cognitive development, helping to optimize individualized therapeutic strategies with timely interventions, thereby enhancing the quality of life for affected individuals.

Email : yaeji.kim@skku.edu   

Eunah Yang, M.S.

M.S. 2024. Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST).
B.S. 2022. Life Scineces, Gwangju Institute of Science and Technology (GIST).

Eunah received her M.S. from the Department of Bio and Brain Engineering at the Korea Advanced Institute of Science and Technology (KAIST) in Daejeon, Korea. Her work used multi-modal neuroimaging techniques, including positron emission tomography (PET) and diffusion tensor imaging (DTI), to examine Parkinson’s disease from a heterogeneous pathological perspective.

She is focused on exploring the transdiagnostic features of neuropsychiatric disorders, particularly in differentiating overlapping features through neuroimaging analysis. She is interested to address the challenges of heterogeneity within and similarities across disorders by examining how to best categorize human behaviors and mental states for more accurate analysis. By applying brain network analysis and techniques with spatial representations, she seeks to refine diagnostic tools and develop personalized interventions, ultimately improving diagnostic accuracy and patient outcomes. 

Jiwon Liana Song, M.S.

M.S. 2026. Artificial Intelligence in Medicine, SAIHST, Sungkyunkwan University.
B.S. 2022. Neuroscience, Carnegie Mellon University.

Liana received her M.S. in Artificial Intelligence in Medicine from Sungkyunkwan University’s Department of Digital Health. Her master’s thesis proposed a pediatric ADHD classification pipeline using source-localized EEG and its multi-modal graph features. Building on this foundation, she is expanding her research interest toward the discovery and analysis of possible ADHD biomarker candidates with AI—leveraging not only EEG but also other neuroimaging modalities like fMRI to build, adapt, and validate computational models that generalize across subjects and clinical settings.

Yongkak Kwon, M.S.

M.S. 2026. Medical Device Management and Research, SAIHST, Sngkyunkwan University.
B.S. 2024. Biomedical Engineering, Yonsei University.

I majored in Biomedical Engineering. I am interested in the growth and development of the human brain in infants, particularly in understanding the neural mechanisms behind these processes, using fMRI-based studies to explore brain connectivity. Additionally, I am fascinated by using various statistical methods to analyze and interpret complex data in developmental research.

DongGyu Kim (MS student, Lab Manager)

B.S. 2025. Computer Engineering, Academic Credit Bank System.

My research interests include the complex mechanisms of human intelligence (what is) , fMRI-based connectome, and brain-inspired AI (how to). My final goal is to understand the working principle of the brain and simulate human brain using computational models. Currently, I am investigating the diagnosis of various psychiatric disorders, such as ADHD and dementia, through the analysis of fMRI-based neural data with state-of-the-art AI model. Additionally, I am involved in brain connectivity (or connectome) analysis, aiming to leverage these findings to advance AI. 

Email : dg3625@naver.com 

Bomin Kim (MS student)

B.A. 2023. Counseling Psychology (double major: Art therapy & minor: Data AI convergence), Cha University.

I majored in psychology and has conducted clinical research on patients with mental disorders across various age groups, from children to the elderly. Currently, I am exploring the interaction between brain network connectivity and cognitive functions, with a particular focus on how neural adaptability influences learning, memory, creativity, and the aging process. Through connectome research, I aim to analyze the structural and functional connectivity of brain networks and investigate how their flexibility and optimization contribute to cognitive regulation.

Hahyun Cho (MS student)

B.S. 2022. Mathematics, Chungnam National University.

Hahyun is dedicated to decoding the complexities of brain function through mathematical and computational approaches. Her research focuses on analyzing fMRI-based connectomics to understand how network organization relates to higher-level cognitive functions like attention and memory. By leveraging computational and data-driven modeling, she aims to map individual differences in brain connectivity to predict clinical and cognitive outcomes.

Minjae Kim (BS student, integrated BS-MS course)

2023-present. Biological Sciences, Sungkyunkwan University.

I aim to understand the operating principles of the human brain using fMRI-based connectome analysis. My research focuses on how higher-order cognitive functions—such as attention, memory, and cognition—are organized and interact at the level of large-scale brain networks. I am particularly interested in computational modeling and simulation approaches to study how perturbations to brain networks, such as virtual lesions, affect cognitive function. Ultimately, I hope to contribute to research that links brain network organization to neurological and cognitive impairments.