Dr. Jiafa Chen is a distinguished researcher in optical engineering, focusing on brain function imaging and optical technology. He is currently part of the ultra-precision optical manufacturing team led by Academician Zhuang Songlin at the Chinese Academy of Engineering. His innovative work at the intersection of psychology and artificial intelligence has earned him recognition in the scientific community.
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🎓 Education:
Dr. Chen graduated with a Ph.D. in Optical Engineering from the University of Shanghai for Science and Technology. His advanced education has equipped him with the expertise to contribute significantly to the field of optical applications and functional brain imaging.
🏢 Experience:
With a robust background in both academia and industry, Dr. Chen has led and completed two major research projects and consulted on eight industry projects. His practical experience is complemented by his active participation in international collaborations with institutions like Nanyang Technological University and the University of Glasgow.
🔬 Research Interest:
Dr. Chen’s research interests include functional brain imaging and optical applications. He is particularly focused on developing novel technologies for emotion recognition through the integration of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG).
🏅 Awards:
Dr. Chen’s contributions have been recognized with several patents and publications. His work in emotion detection using innovative system architecture and signal processing has set new standards in the field.
📝 Publications Top Notes:
A Novel Wearable Dual-Modal Device for Real-Time Emotion Recognition – Optical Communications, 2022. Cited by 10 articles.
Functional Brain Imaging with fNIRS-EEG Ensemble – Journal of Neuroscience Methods, 2021. Cited by 8 articles.
Innovations in Optical Technology for Emotion Detection – Optics and Laser Engineering, 2020. Cited by 15 articles.
Enhancing Emotional Feature Extraction via TCN – Signal Processing, 2019. Cited by 20 articles.
Portable Systems for Brain Function Monitoring – Biomedical Signal Processing and Control, 2018. Cited by 12 articles.