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Muhammad Zubair
Research topics- # Affective computing, Biomedical signal processing, Deep learning, Representation learning, Explainable AI (XAI), Neurodegenerative Disorders
- I earned my B.Sc. in Electrical Engineering from the University of Engineering and Technology, Pakistan, followed by an integrated M.S. and Ph.D. in Information and Communication Technology from the University of Science and Technology (UST), South Korea. Previously, at ETRI and Motov Co., Ltd., I developed domain-adaptive algorithms for physiological data, multimodal emotion recognition systems, and healthcare applications such as arrhythmia detection and stress detection. Currently, I am a postdoctoral researcher at KAIST, where my research focuses on developing AI-driven models for Alzheimer’s disease prediction through EEG analysis, MRI/PET imaging, and advanced representation learning. My research interests include deep representation learning, generative AI, and explainable AI for healthcare. I aim to advance AI-enabled neurodiagnostics and precision healthcare for brain-related disorders.
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남선구
Research topics- # VR Perception
- # AR Perception
- # BCI
- # EEG
- # Cybersickness
- 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.
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신우리
Research topics- # Neuroarchitecture
- # Affordance
- # Architectural affordance
- # Cognitive neuroscience
- 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.
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김평수
Research topics- # Connectome
- # Caenorhabditis elegans
- # Complex network
- # Graph theory
- # Asymmetry
- # fMR
- 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.
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김선일
Research topics- # Data-Driven Science
- # Dynamical Systems
- # EEG
- # The General Theory of Brain Dynamics
- # Statistical Physics
- # Subjective Probability Theory
- 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.
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이정민
Research topics- # Social communication
- # Social interactions, gossip
- # Cognitive neurosience
- # fMRI
- Jeungmin obtained a Bachelor's degree in Biology from the University of British Columbia in Canada. In the final semester of her undergraduate studies, she happened to take a cognitive science course and became fascinated by one particular chapter of human cognition that led her to delve deeper into the study of human cognition and social interactions. During her graduate school years at KAIST, she conducted in-depth research on behavioral motives and neural mechanisms of social information processes and social communication using fMRI and computational models. She is planning to further understand the human mind and brain in terms of social decision-making and cognitive functions.
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장상진
Research topics- # Brain-computer interface (BCI)
- # Brain-machine interface (BMI)
- # Robotic arm
- # Trajectory
- # EEG
- # Imagined movement
- During my undergraduate studies, I majored in Biochemistry and Cell Biology at The Hong Kong University of Science and Technology (HKUST). Towards graduation, I became interested in neuroscience and its applications to brain-computer interface (BCI), especially on the decoding of imagined hand movement. I investigated on the topic for my Master’s study at the Department of Bio and Brain Engineering at Korea Advanced Institute of Science and Technology (KAIST), under the supervision of Professor Jaeseung Jeong at Decision Brain Dynamics Lab. I continued on this topic for my PhD study as well, using both the invasive and noninvasive human neural signals and explored the possibilities of using these signals for robotic arm trajectory control. Through these explorations, ultimately, I envision designing a BCI that allows a user to control a robotic arm using only thoughts, without any movement or physical interface operation, such that the system can provide useful assistance for people with upper limb motor disabilities.