当前位置：
吴畏，资深研究员
邮箱: weiwuneuro@sjtu.edu.cn
研究方向: 脑疾病精准诊疗及临床转化、脑信号处理与机器学习
教育经历
2006-2012年 清华大学生物医学工程系，博士(麻省理工学院联合培养)
2003-2006 年 清华大学生物医学工程系，硕士
1999-2003 年 南京邮电大学信息工程系，学士
工作经历
2024年-至今 欧洲杯竞猜平台松江研究院，资深研究员
2020-2024年 美国Alto Neuroscience公司(纽交所上市) ,联合创始人
2021-2021年 美国南卡罗来纳州立大学(R1研究型大学)计算机科学与工程系,副教授
2018-2020 年 斯坦福大学精神疾病与行为学系，讲师(Instructor)
2012-2018 年 华南理工大学自动化科学与工程学院，副教授/教授
主要学术成绩及奖励
吴畏研究员在学术界和工业界均拥有丰富经验，入选国家级海外高层次领军人才计划及上海市领军人才（海外）创新人才计划。曾获广东省自然科学一等奖（排名第二），现任IEEE生物医学信号处理技术委员会委员，并担任多本国际顶级期刊的编委，包括IEEE Transactions on Affective Computing和IEEE Journal of Biomedical and Health Informatics。作为通讯作者或第一作者，在Nature Biotechnology、Nature Biomedical Engineering、Nature Mental Health、Science Translational Medicine、IEEE Transactions on Pattern Analysis and Machine Intelligence以及IEEE Signal Processing等生物医学工程与人工智能领域的SCI期刊上发表了数十篇高水平论文，尤其在脑电信号分析算法和精神疾病诊疗生物标志物研究方面做出了重要贡献。
吴畏研究员是精神疾病精准诊疗公司 Alto Neuroscience（纽交所上市）的联合创始人及前任首席数据科学官，构建了该领域首个精神疾病精准诊疗生物标志物转化平台，并共同领导了多项精神疾病新药的临床试验。成功识别并前瞻性验证了多种脑环路疗效预测生物标志物，为精神疾病诊疗迈入精准医学时代作出了开创性贡献，是这一领域的国际领军人物。
吴畏，资深研究员
一、 脑疾病的精准诊疗及临床转化,包括1)精神病高危人群风险预测;2)青少年抑郁早筛早干预;3)睡眠障碍相关脑疾病的精准诊疗.
二、 脑信号处理与机器学习,包括1)脑信号解码算法;2)脑电/脑磁源定位算法.
-
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).
-
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.
-
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).
-
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).
-
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)
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
13. Wu W, Nagrajan S, Chen Z. Bayesian Machine Learning for EEG/MEG. IEEE Signal Processing Magazine, 2016, 33(1): 14-36.
-
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.