Prof. Jing Sui | Beijing Normal University | China
Professor Jing Sui has established herself as a pioneering figure in computational psychiatry and cognitive neuroscience. With a strong foundation in optical engineering, image processing, and computer science, she built her career across leading institutions in the United States and China. Her research contributions lie at the forefront of multimodal fusion, brain imaging data mining, and the application of machine learning and deep learning to mental health studies. By developing innovative methods for biomarker identification, she has advanced diagnostic precision in psychiatry and neurological research. Recognized internationally through numerous awards, top citations, and global rankings, she has played a vital role in shaping both research and mentorship within the field.
Profile
Early Academic Pursuits
From the beginning of her academic journey, Jing Sui demonstrated a strong aptitude for both engineering and computational sciences. She trained in optical technology and photoelectric instrumentation, while also developing parallel expertise in computer science. Her doctoral work in optical engineering, with a focus on image and signal processing, laid the foundation for her lifelong interest in extracting meaningful patterns from complex brain data. This multidisciplinary background positioned her uniquely at the intersection of neuroscience, engineering, and data science.
Professional Endeavors
Her professional career has spanned leading institutions in both China and the United States. She began as a postdoctoral fellow and later advanced to research scientist and assistant professor at a pioneering brain research network in the United States. Returning to China, she took on leadership roles at the Chinese Academy of Sciences, where she established herself as a principal investigator. Later, she became a professor at prominent national universities, where she continues to mentor and guide future generations of neuroscientists. These roles have enabled her to bridge international research collaborations and foster innovation in computational psychiatry.
Contributions to Cognitive Neuroscience
At the core of her scientific contributions lies the use of advanced data-driven methods to better understand the human brain. She has made notable advances in multimodal fusion techniques, combining diverse forms of neuroimaging data to capture a more holistic view of brain function. Her work integrates signal processing, independent component analysis, and deep learning to uncover hidden patterns that inform the study of mental disorders. By pushing the boundaries of machine learning and multivariate modeling, she has contributed significantly to the field of brain imaging data mining and its translation into clinical research.
Research Focus in Computational Psychiatry
Her research is strongly anchored in the identification of biomarkers for mental health conditions. By applying artificial intelligence to large-scale imaging datasets, she has advanced methods for detecting subtle brain alterations linked to psychiatric and neurological disorders. This approach has enhanced the precision of diagnostic tools and informed the development of computational psychiatry as a discipline. Her work illustrates how brain-inspired intelligence can merge with clinical practice to improve patient outcomes, offering pathways toward personalized mental health care.
Accolades and Recognition
Her groundbreaking contributions have been recognized nationally and internationally. She has received top-tier awards for natural sciences, science and technology innovation, and contributions to cancer-related brain imaging research. Prestigious foundations have supported her as a leading young scientist, while multiple academic societies have acknowledged her excellence through best paper awards, top-cited distinctions, and conference recognitions. She has also been consistently ranked among the world’s leading neuroscientists, reinforcing her reputation as a trailblazer in computational psychiatry and neuroimaging.
Impact and Influence
Her influence extends beyond her own discoveries to shaping the global research community. As a mentor and leader, she has cultivated young researchers who continue to expand the field of cognitive neuroscience. She has been instrumental in bringing together expertise from imaging, engineering, and psychiatry, creating an integrative approach that strengthens interdisciplinary collaboration. Her pioneering methods are widely adopted by neuroscientists worldwide, serving as a benchmark for brain imaging and machine learning studies.
Legacy and Future Contributions
The legacy of her work lies in redefining how brain imaging data can be harnessed to advance mental health research. By blending computational innovation with clinical relevance, she has carved a path that others continue to follow. Looking ahead, her contributions are likely to further transform computational psychiatry, particularly as advances in artificial intelligence deepen. Her future work will continue to shape the next generation of neuroscientific discovery, offering new insights into the biological basis of mental health and paving the way for more effective interventions.
Publications
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls – MR Arbabshirani, S Plis, J Sui, VD Calhoun – 2017
Multimodal fusion of brain imaging data: a key to finding the missing link (s) in complex mental illness – VD Calhoun, J Sui – 2016
A review of multivariate methods for multimodal fusion of brain imaging data – J Sui, T Adali, Q Yu, J Chen, VD Calhoun – 2012
Machine learning in major depression: From classification to treatment outcome prediction – S Gao, VD Calhoun, J Sui – 2018
NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders – Y Du, Z Fu, J Sui, S Gao, Y Xing, D Lin, M Salman, A Abrol, MA Rahaman, … – 2020
Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises – J Sui, R Jiang, J Bustillo, V Calhoun – 2020
Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder – VD Calhoun, J Sui, K Kiehl, J Turner, E Allen, G Pearlson – 2012
Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model – J Sui, G Pearlson, A Caprihan, T Adali, KA Kiehl, J Liu, J Yamamoto, … – 2011
Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia – Q Yu, EB Erhardt, J Sui, Y Du, H He, D Hjelm, MS Cetin, S Rachakonda, … – 2015
A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia – H Yang, J Liu, J Sui, G Pearlson, VD Calhoun – 2010
Interaction among subsystems within default mode network diminished in schizophrenia patients: a dynamic connectivity approach – Y Du, GD Pearlson, Q Yu, H He, D Lin, J Sui, L Wu, VD Calhoun – 2016
Function–structure associations of the brain: evidence from multimodal connectivity and covariance studies – J Sui, R Huster, Q Yu, JM Segall, VD Calhoun – 2014
Distinct and common aspects of physical and psychological self-representation in the brain: A meta-analysis of self-bias in facial and self-referential judgements – C Hu, X Di, SB Eickhoff, M Zhang, K Peng, H Guo, J Sui – 2016
Conclusion
Professor Jing Sui’s work demonstrates the transformative power of combining engineering, neuroscience, and artificial intelligence in understanding the human brain. Her contributions have not only advanced computational psychiatry but also created pathways for practical clinical applications. Through her leadership, mentorship, and groundbreaking research, she has left an enduring impact on global neuroscience. Her continued efforts are poised to deepen the integration of brain-inspired intelligence with mental health care, ensuring her legacy as a leading innovator in the field.