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. 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.

Chae Young Im, M.D. (PhD student)

M.S.  2023.  School of Medicine. Sungkyunkwan University.
M.D.  2019.  School of Medicine. Ajou University.

Chae Young holds a master's degree from Sungkyunkwan University School of Medicine and is a board-certified radiologist, having completed her residency training at Samsung Medical Center. With a solid research background in the discovery and evaluation of imaging biomarkers for clinical diagnosis and prognosis through machine learning and AI modeling, she is currently serving as a Clinical Fellow in the Neuroradiology Section at Samsung Medical Center. Her primary research focuses on investigating structural and functional MR imaging biomarkers to elucidate the heterogeneity of neurodegenerative diseases, identify potential candidates for clinical interventions, and estimate their effects.

Email : chaeyoungcp3.lim@samsung.com 

Yongkak Kwon (MS student)

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. 

Email : yongkak@skku.edu 

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.

Minjae Kim

2023-present.  B.S.  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.

Hahyun Cho

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.

Seungjoo Lee

present.  B.A.  Psychology. Boston University.

I am majoring in Psychology at Boston University and have a strong interest in the field of neuroscience. My academic focus lies in child development, particularly how early experiences shape emotional and cognitive growth, as well as in neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. I am especially interested in using fMRI and connectome-based approaches to investigate how changes in brain network connectivity relate to cognition and behavior. I hope to better understand brain function across development, ultimately contributing to the early detection of psychiatric disorders.

Yejun Park

present.  B.S.  Biomedical Engineering. Boston University.

I am an undergraduate student at Boston University with interests in network neuroscience and healthcare. My current research focuses on brain connectomes and cognitive state transformation using fMRI data. My long-term goal is to translate neuroscience and AI research into innovative healthcare technologies that improve disease diagnosis, treatment, and patient care.