김선일 Ph.D candidate

Research topics

• # Data-Driven Science
• # Dynamical Systems
• # EEG
• # The General Theory of Brain Dynamics
• # Statistical Physics
• # Subjective Probability Theory

Biosketch

• Sunil Kim completed both his undergraduate and master's studies at the Department of Physics at Konkuk University in Seoul. During the time, he engaged in statistical mechanical investigations of electron dynamics in complex mediums.
• Currently, he is pursuing his doctoral studies under the guidance of Professor Jaeseung Jeong at the Department of Brain and Cognitive Sciences at KAIST. His research primarily involves the analysis of biosignals such as EEG and EMG through the application of non-linear dynamics techniques.
• More specifically, his work utilizes data-driven science algorithms, such as the Sparse Identification of Nonlinear Dynamics (SINDy), to discover a set of differential equations that can aptly describe given biosignals. His research aims to interpret these equations in a dimension that has physiological significance.
• Through this analytical approach, he aspires to offer physiological or physical interpretability to currently employed "black box" AI models.
• His grand vision, rooted in this newfound interpretability, is to elucidate a General Theory of Brain Dynamics, a breakthrough that could fundamentally transform our understanding of the brain's inner workings.

김평수 Ph.D candidate

Research topics

• # Connectome
• # Caenorhabditis elegans
• # Complex network
• # Graph theory
• # Asymmetry
• # fMR

Biosketch

• I graduated from KAIST with a major in Bio and Brain Engineering and am now pursuing a Ph.D. under the supervision of Professor Jaeseung Jung. The primary area of focus for me is connectome, a comprehensive cartography of neural connections within an organism's brain or nervous system. This map provides a detailed blueprint of the neural architecture, facilitating the study of brain function and information processing. In my studies, I am particularly interested in the structural asymmetry of the Caenorhabditis elegans connectome and how it connects to functional asymmetry. My objective is to identify network properties that can enhance our understanding of asymmetry, with potential implications for the connectome of complex organisms, including humans.

송영조 Ph.D candidate

Research topics

• # Statistical learning in human brain
• # Motor learning
• # fMRI
• # Voxel-based morphometry
• # Multivariate brain-imaging analysis
• # Theoretical neuroscience

Biosketch

• My scholarly interests range from the details, such as analyzing learning profiles during specific contexts (e.g., sensorimotor adaptation, social interaction), to developing theories that can describe general aspects of cognitive functioning, such as statistical learning in the brain or the Bayesian brain.

신우리 Ph.D candidate

Research topics

• # Neuroarchitecture
• # Affordance
• # Architectural affordance
• # Cognitive neuroscience

Biosketch

• I graduated from KAIST with a major in Civil and Environmental Engineering and am currently pursuing a Ph.D. in Brain and Cognitive Engineering under Professor Jaesung Jung's supervision. My main interest lies in Neuroarchitecture, which involves the integration of architecture and neuroscience. My research focuses on Architectural Affordance, which explores the relationship between humans and architectural elements. I aim to uncover how users perceive and interact with the environment. The ultimate goal is to create user-centered, improved spaces based on these findings.

남선구 Ph.D candidate

Research topics

• # VR Perception
• # AR Perception
• # BCI
• # EEG
• # Cybersickness

Biosketch

• The ultimate goal of my research is to observe the Brain Dynamics in various situations such as VR video, target recognition, lie detection, and to gain new inspiration from it. To this end, I am working to find answers through machine learning and deep learning by collecting data using brain imaging technologies such as EEG, fMRI.

김신 Ph.D candidate

Research topics

• # Neuropsychiatry
• # Adolescent development
• # Genetics
• # Machine learning

Biosketch

• My goal is to develop a predictive model of psychiatric disorders based on individuals' neurobiological, genetic, and environmental information. This model would not only let us better understand causes of psychiatric disorder but also prevent them by identifying risk factors.

김재현 Ph.D candidate

Research topics

• # Human & AI planning
• # Deep learning
• # Reinforcement learning
• # Brain-computer interface
• # Data-driven avatar control

Biosketch

• My ultimate research goal is to find the general-purpose planning mechanisms of human brains, apply them to the algorithms of artificial intelligence, and enable communications between a human agent and AI through common latent representations that they learn. To realize it, I am interested in machine learning methods applicable to a range of practical domains, including Brain-computer interface, Robotics, Computer graphics, and Computer vision.

최병혁 Ph.D candidate

Research topics

• # Alzheimer's disease
• # Cognitive science
• # Age & sensory-related Cognitive Research
• # Behavior-Based Cognitive Research
• # Machine learning based brain network analysis

Biosketch

• I am a graduate of the Department of Biological Sciences at KAIST with a keen interest in neurodegenerative diseases and their treatments. Currently, I am conducting research to classify subtypes of dementia patients using machine learning. Based on this, in the future, I plan to conduct research on dementia and anti-aging prevention through sensory and external stimuli. Ultimately, my goal is to contribute to research that enables humanity to control a range of neurodegenerative brain diseases, including dementia, by preventing aging.

Doyin Ph.D candidate

Research topics

• # Social Curiosity
• # Social Decision making
• # fMRI
• # Suicide
• # Mental disorders
• # Machine Learning Applications

Biosketch

• I completed my bachelor, and master's degrees at the KAIST Department of Bio and Brain Engineering, where I was introduced to the world of Neuroscience. During this period I focused on cognitive neuroscience, and related opportunities for machine learning applications, such as in the field of computational neuropsychiatry. As a member of Professor Jaeseung Jeong's decision-brain dynamics lab, my attention is centered on the neural mechanisms underlying social curiosity, and its influences on learning, interaction, perception, and decision-making in the social domain. I believe that this knowledge can potentially be leveraged to address the social impairments prevalent in numerous mental disorders, ultimately enhancing the lives of those affected. I also remain deeply interested in the application of machine learning techniques toward a better understanding and early diagnosis of mental disorders and impaired cognitive functioning. Through my research, I aim to contribute to improving the quality of life for individuals navigating the complexities of cognitive and social impairments.

정미르 Ph.D candidate

Research topics

• # Neuropsychiatric disorder
• # Graph theory

Biosketch

• My research aim is to detect differences in the brain topological patterns between autism spectrum disorder and controls. Graph theory analysis is a tool to describe brain structure and function.

정준하 Ph.D candidate

Research topics

• # Brain-Computer Interface
• # EEG Analysis
• # Machine Learning
• # Deep Learning

Biosketch

• Jun Ha (Rachel) Jung is currently a Ph.D. candidate at KAIST, focusing her research on the field of brain-computer interface (BCI). With a strong academic foundation from Dartmouth, where she obtained her bachelor's degree, Rachel's passion lies in understanding and analyzing EEG signals to advance the field of BCI. Driven by a desire to make a meaningful impact on the lives of individuals affected by stroke, Rachel's current research goal is to decode the brain signals of stroke patients. By deciphering their intentions through these signals, she aims to develop a system that enables them to control a prosthetic robotic arm, restoring their ability to interact with the world around them. Through her work, she aspires to contribute to the development of innovative technologies that bridge the gap between the human mind and machines.

박재빈 Ph.D candidate

Research topics

• # Resilience
• # Suicide
• # Psychological Pain
• # Mental Disorders

Biosketch

• Majoring in Psychology at McGill University, Jae Bin had the opportunity to create intervention programs for young adults that aimed to improve their mental health and also handle real-life stressors. His current research interest includes resilience and suicide and eventually, Jae Bin hopes to study and develop empirically-supported interventions programs that can help improve the resilience of individuals

Luliia M.S

Research topics

• # Intertemporal choice
• # Risk Preference
• # Ambiguity Aversion

Biosketch

• By acquiring her Bachelor’s degree in Biology and Genetics, Yulia has shaped her academic interests in the peculiarities of human cognitive function and the neural mechanisms underlying the most perplexing processes such as economic decision-making including the most relevant cognitive biases expressed in human choice behaviours. At the prof. Jaeseung Jeong’s Brain Dynamics lab, she aims to focus her research on the neural substrates of the intertemporal choice intererwined with risk preference and ambiguity aversion - the idea of correlation proposed in various behavioural economic models. Studying economic decision making is crucial in terms of human behavior because it helps us understand how individuals make choices, allocate resources, respond to incentives, and pursue their goals, providing valuable insights into decision-making processes that can be applied to various domains beyond economics, such as psychology, sociology, and public policy.