當(dāng)前位置：
吳畏,，資深研究員,，國家海外高層次人才
研究方向: AI驅(qū)動的腦疾病精準(zhǔn)診療及臨床轉(zhuǎn)化,、腦信號處理與機(jī)器學(xué)習(xí)
教育經(jīng)歷
2006-2012年 清華大學(xué)生物醫(yī)學(xué)工程系,，博士(麻省理工學(xué)院聯(lián)合培養(yǎng))
2003-2006 年 清華大學(xué)生物醫(yī)學(xué)工程系,，碩士
1999-2003 年 南京郵電大學(xué)信息工程系,，學(xué)士
工作經(jīng)歷
2024年-至今 上海交通大學(xué)醫(yī)學(xué)院松江研究院,，資深研究員
2020-2024年 美國Alto Neuroscience公司(紐交所上市) ,聯(lián)合創(chuàng)始人
2012-2019 年 華南理工大學(xué)自動化科學(xué)與工程學(xué)院,，副教授,、教授
主要學(xué)術(shù)成績及獎(jiǎng)勵(lì)
吳畏，資深研究員,，博士生導(dǎo)師,。在學(xué)術(shù)界和工業(yè)界均擁有豐富經(jīng)驗(yàn)，國家海外高層次人才,，曾獲廣東省自然科學(xué)一等獎(jiǎng)（排名第二）,，現(xiàn)任IEEE生物醫(yī)學(xué)信號處理技術(shù)委員會委員,，并擔(dān)任多本國際頂級期刊的編委，包括IEEE Transactions on Affective Computing和IEEE Journal of Biomedical and Health Informatics,。作為通訊作者或第一作者,，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 magazine等生物醫(yī)學(xué)工程與人工智能領(lǐng)域的SCI期刊上發(fā)表了數(shù)十篇高水平論文,，尤其在腦電信號分析算法和精神疾病診療生物標(biāo)志物研究方面做出了重要貢獻(xiàn),。
吳畏研究員是精神疾病精準(zhǔn)診療公司Alto Neuroscience（紐交所上市）的聯(lián)合創(chuàng)始人和前任首席數(shù)據(jù)科學(xué)官。構(gòu)建了該領(lǐng)域首個(gè)精神疾病診療AI生物標(biāo)志物轉(zhuǎn)化平臺,，共同領(lǐng)導(dǎo)了多項(xiàng)精神疾病新藥的臨床試驗(yàn),，成功識別并前瞻性驗(yàn)證了腦環(huán)路療效預(yù)測AI生物標(biāo)志物，推動了精神疾病診療邁入精準(zhǔn)醫(yī)學(xué)時(shí)代,。
吳畏,，資深研究員
一、 腦疾病的精準(zhǔn)診療及臨床轉(zhuǎn)化,包括1)精神病高危人群風(fēng)險(xiǎn)預(yù)測;2)青少年抑郁早篩早干預(yù);3)睡眠障礙相關(guān)腦疾病的精準(zhǔn)診療.
二,、 腦信號處理與機(jī)器學(xué)習(xí),包括1)腦信號解碼算法;2)腦電/腦磁源定位算法.
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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.
