삼성융합의과학원 - 삼성융합의과학원

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학력

  • (Ph.D.) 2015. 공학박사, 바이오및뇌공학, KAIST.

약력/경력

  • 2017 - 2021. Post-doc, Yale University, USA.
  • 2021 - 2023. Associate Research Scientist (Research Faculty), Yale University, USA.
  • 2023 - 현재. 조교수, 성균관대학교 삼성융합의과학원 디지털헬스학과
  • 2023 - 현재. 조교수, 삼성서울병원 미래의학연구원 데이터사이언스연구소

학술지 논문

  • (2024)  Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention.  JOURNAL OF NEUROSCIENCE.  44,  14
  • (2023)  Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank.  LANCET DIGITAL HEALTH.  5,  6
  • (2022)  Differences in the functional brain architecture of sustained attention and working memory in youth and adults.  PLOS BIOLOGY.  20,  12
  • (2022)  A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome.  NEUROIMAGE.  257, 
  • (2022)  A brain-based general measure of attention.  NATURE HUMAN BEHAVIOUR.  6, 
  • (2022)  Antagonistic network signature of motor function in Parkinson's disease revealed by connectome-based predictive modeling.  NPJ PARKINSONS DISEASE.  8, 
  • (2021)  Predicting multilingual effects on executive function and individual connectomes in children: An ABCD study.  PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA.  118,  49
  • (2019)  Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors.  NEUROIMAGE.  197, 
  • (2018)  Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets.  NEUROIMAGE.  167, 
  • (2017)  Degree-based statistic and center persistency for brain connectivity analysis.  HUMAN BRAIN MAPPING.  38,  1