Location:
Wei Wu, Ph.D.
Senior Principal Investigator
Email: [email protected]
Research Interests：
AI-driven precision brain health and clinical translation, brain signal processing, machine learning.
Education
2006–2012 Ph.D. in Biomedical Engineering, Tsinghua University (Joint Program with MIT)
2003–2006 M.S. in Biomedical Engineering, Tsinghua University
1999–2003 B.S. in Information Engineering, Nanjing University of Posts and Telecommunications
Professional Experience
2024–Present Senior Principal Investigator, Songjiang Research Institute, Shanghai Jiao Tong University School of Medicine
2020–2024 Co-Founder and Chief Data Science Officer, Alto Neuroscience (NYSE-listed)
2012–2019 Associate Professor & Professor, School of Automation Science and Engineering, South China University of Technology
Academic Achievements and Awards
Dr. Wei Wu is a senior principal investigator with extensive experience in both academia and industry. As a National High-Level Overseas Talent, he has been recognized with the First Prize of Guangdong Provincial Natural Science Award. He currently serves as a member of the IEEE Technical Committee on Biomedical Signal Processing and as an editorial board member for several top-tier international journals, including IEEE Transactions on Affective Computing and IEEE Journal of Biomedical and Health Informatics.
As the corresponding or first author, Dr. Wu has published dozens of high-impact papers in leading journals in biomedical engineering and artificial intelligence, including Nature Biotechnology, Nature Biomedical Engineering, Nature Mental Health, Science Translational Medicine, IEEE Transactions on Pattern Analysis and Machine Intelligence, and IEEE Signal Processing Magazine. His work has made significant contributions to EEG signal analysis algorithms and biomarker research for psychiatric disorder diagnosis and treatment.
Dr. Wu is also the Co-Founder and former Chief Data Science Officer of Alto Neuroscience, a NYSE-listed precision psychiatry company. He developed the first AI-driven biomarker translation platform for psychiatric diagnosis and co-led multiple clinical trials for novel psychiatric drugs. His research successfully identified and prospectively validated AI biomarkers for treatment response prediction based on brain circuits, pioneering the application of precision medicine in psychiatry.
Wu Wei
AI-Driven Precision Brain Health and Clinical Translation, including:
Brain Signal Processing and Machine Learning, including:
Early Prediction of conversion to schizophrenia for the clinical high risk (CHR) population
Early screening and intervention for adolescent depression
Precision diagnosis and treatment of sleep disorder-related brain diseases
Brain signal decoding algorithms
EEG/MEG source localization algorithms
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1. Wu W, Zhang Y, Jiang J, Lucas V M, Fonzo G A, Rolle C E, Cooper C, Chin-Fatt C, Krepel N, Cornelssen C A, Wright R, Toll R T, Trivedi H M, Monuszko K, Caudle T L, Sarhadi K, Jha M K, Trombello J M, Deckersbach T, Adams P, McGrath P J, Weissman M M, Fava M, Pizzagalli D A, Arns M, Trivedi M H, Etkin A. An Electroencephalographic Signature Predicts Antidepressant Response in Major Depression. Nature Biotechnology, 2020, 38(4): 439-447. (Highlighted by News & Views in Nature Biotechnology: https://doi.org/10.1038/s41587-020-0476-5; Reviewed in Psychiatry Times: https://www.psychiatrictimes.com/view/homing-eeg-signature-predict-antidepressant-response; Media coverage by NIH, Stanford University, Scientific American, NPR, USNews, The Times, Time, and Psychiatric Times).
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2. Zhang Y#, Naparstek S, Gordon J, Watts M, Shpigel E, EI-Said D, Badami F, Eisenberg M, Toll R, Gage A, Goodkind M, Etkin A#, Wu W#. Machine learning-based identification of a psychotherapy-predictive electroencephalographic signature in PTSD. Nature Mental Health, 2023, 1: 284-294.
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3. Zhang Y*, Wu W*, Toll R T, Naparstek S, Maron-Katz A, Watts M, Gordon J, Jeong J, Astolfi L, Shpigel E, Longwell P, Sarhadi K, El-Said D, Li Y, Cooper C, Chin-Fatt C, Arns M, Goodkind M S, Trivedi M H, Marmar C R, Etkin A. Identification of Psychiatric Disorder Subtypes from Functional Connectivity Patterns in Resting-State Electroencephalography. Nature Biomedical Engineering, 2021, 5(4): 309-323. (Media coverage by Forbes).
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4. Etkin A*, Maron-Katz A*, Wu W*, Fonzo G A*, Huemer J*, Vertes P E*, et al. Using fMRI Connectivity to Define a Treatment-Resistant Form of Post-Traumatic Stress Disorder. Science Translational Medicine, 2019, 11 (486) (Highlighted by Nature Human Behaviour: https://doi.org/10.1038/s41562-019-0627-1).
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5. Tian W*, Zhao D*, Ding J, Zhan S, Zhang Y, Etkin A, Wu W#, Yuan T#, An electroencephalographic signature predicts craving for methamphetamine. Cell Reports Medicine, 2024, 5(2): 101427. (Highlighted by Preview in Cell Reports Medicine: https://doi.org/10.1016/j.xcrm.2024.101427)
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6. Wang W*, Qi F*, Wipf D, Cai C, Yu T, Li Y, Yu Z#, Wu W#. Sparse Bayesian Learning for End-to-End EEG Decoding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(12): 15632-15649.
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7. Wang W, Qi F, Huang W, Li Y, Yu Z, Wu W#. EEG-based Cross-subject Emotion Recognition Using Sparse Bayesian Learning with Enhanced Covariance Alignment. IEEE Transactions on Affective Computing, in press.
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8. Huang G, Liu K, Liang J, Cai C, Gu Z, Qi F, Li Y, Yu Z, Wu W#. Electromagnetic source imaging via a data-synthesis-based convolutional encoder-decoder network. IEEE Transactions on Neural Networks and Learning Systems, 2023, 35(5): 6423-6437.
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9. Huang W, Wang W, Li Y, Wu W#. FBSTCNet: A spatial-temporal convolutional network integrating power and connectivity features for EEG-based emotion decoding. IEEE Transactions on Affective Computing, 2024, DOI: 10.1109/TAFFC.2024.3385651.
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10. Qi F, Wu W#, Yu Z, Gu Z, Wen Z, Yu T, Li Y. Spatio-Temporal Filtering-Based Channel Selection for Single-Trial EEG Classification. IEEE Transactions on Cybernetics, 2021, 51(2): 558-567.
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11. Qi F, Li Y, Wu W#. RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(12): 3070-3082.
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12. Toll R T*, Wu W*, Naparstek S, Zhang Y, Narayan M, Patenaude B, De Los Angeles C, Sarhadi K, Anicetti N, Longwell P, Shpigel E, Wright R, Newman J, Gonzalez B, Hart R, Mann S, Abu-Amara D, Sarhadi K, Cornelssen C, Marmar C, Etkin A. An Electroencephalography Connectomic Profile of Post-Traumatic Stress Disorder. American Journal of Psychiatry, 2020, 177(3): 233-243.
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13. Wu W, Nagrajan S, Chen Z. Bayesian Machine Learning for EEG/MEG. IEEE Signal Processing Magazine, 2016, 33(1): 14-36.
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14. Wu W, Chen Z, Gao X, Li Y, Brown E, Gao S. Probabilistic Common Spatial Patterns for Multichannel EEG Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 639-653.
