Summary
Song Qi is a research fellow at NIMH with a Ph.D. in Social and Decision Neuroscience from Caltech and a decade of experience probing how humans make decisions under threat and social influence. He combines ecologically inspired behavioral paradigms, fMRI, and computational modeling to reveal how cognitive processes and mood disorders interact, applying regression, clustering, and predictive modeling to neural and behavioral datasets. Proficient in R and Python, he leverages NumPy, SciPy, scikit-learn and TensorFlow to turn complex neuroimaging data into interpretable models and visualizations. His trajectory spans top labs at Caltech, Columbia, and international collaborators, reflecting both deep neuroimaging expertise and engineering rigor from an electrical engineering undergraduate background. Colleagues value his ability to translate theoretical questions about fear and decision-making into reproducible computational pipelines and experimentally grounded insights.
10 years of coding experience
6 years of employment as a software developer
California Institute of Technology
Master’s Degree, Cognitive Psychology, Master’s Degree, Cognitive Psychology at Columbia University in the City of New York
Bachelor of Engineering (BEng), Electrical and Electronics Engineering, Bachelor of Engineering (BEng), Electrical and Electronics Engineering at University of Electronic Science and Technology of China
English, Chinese, Japanese