Hae-Jeong Park
Professor
Current position
Professor, Department of Nuclear Medicine, Yonsei University College of Medicine
Research interests
- Integrating systems and theoretical neurosciences to model neuro-cognition and disease mechanism.
- Optimal brain control based on brain theory and computational modelling of brain disease, computational neuropharmacology.
- Facilitating network reorganization using brain decoding (fMRI, EEG), neuromodulation (TMS, tACS), neurofeedback.
- Neurobiological research on interoception and emotional flexibility, linguistic, communicative brains, and digital therapy.
- Artificial intelligence for medical images, molecular brain science based on nuclear medicine.
Hae-Jeong Park is a brain scientist affiliated with the Graduate School of Medical Science, the Department of Nuclear Medicine at Yonsei University College of Medicine, and the Department of Cognitive Science at Yonsei University. He develops precise analysis methods for fMRI, PET and EEG and works on systems, computational and cognitive brain science.
After publishing a whole-brain human fibre map based on diffusion-tensor imaging (2003), he pursued brain-network research and, on invitation from SCIENCE in 2013, wrote a review on structural and functional brain networks and how the brain works (Park and Friston, 2013). His current work combines theoretical neurobiology and computational neuroscience in dynamic effective-connectivity modelling, nonlinear dynamics and control, with the aim of clinical diagnosis and treatment.
He has also studied the social usefulness of cognitive neuroscience through neuroimaging-based work on cognition, neurolinguistics, design and forensic science. With a strong interest in neuroart, visual results from his research have appeared on several journal covers, and he took part in the first neuroart exhibition project.
Since developing an automated expert system for polysomnography during his doctoral work, he has introduced a series of methods for brain imaging, including statistical analysis of cortical EEG activity, the first whole-brain DTI tractography map, ICA-based diagnosis in nuclear neuromedicine and surface-based PET analysis, releasing the software openly. On this basis he developed MNET, a big-data mining pipeline that builds and analyses structural and functional connectomes from human, monkey and rodent imaging.
He builds early-diagnosis systems for populations vulnerable to cognitive-control deficits and VR/web platforms for cognitive, emotional and behavioural assessment, and applies brain-inspired AI to automated diagnosis of retinal, ear and brain images while teaching AI to clinicians. He enjoys generating ideas with collaborators from clinical medicine, biomedical engineering, statistics, psychology, cognitive science, communication and education, opening new research areas through multidisciplinary and international collaboration.