周济，教授，英国东安格利亚大学（University of East Anglia）副教授（荣誉），博导。英国生物科学理事会（BBSRC）下属厄尔汉姆研究中心（Earlham Institute）准终身研究员（group leader），实验室主任（The Zhou Laboratory）。英国国家重大研究项目“设计将来的小麦”（Designing Future Wheat，2017-2022）唯一指定中国研究员（小麦表型组研究子项负责人）。1999年毕业于上海市工程技术大学计算机控制专业，获工学学士学位。2005年于英国东安格利亚大学计算机学院获信息系统硕士学位。2011年于英国东安格利亚大学获计算机科学博士学位（博士由Aviva保险集团和东安格利亚大学国际学生奖学金共同资助）。1999年至2009年，在中英两国业界担任多项职位，包括多媒体软件开发及培训、系统分析师和资深软件项目顾问等。
2011-2014年在英国塞恩斯伯里实验室（The Sainsbury Laboratory，TSL）担任生物信息博士后研究员。主要负责通过高通量共聚焦显微镜、高通量分子筛选系统开展基于细胞表型组学的植物抗病遗传机制等方面的研究。2014-至今在约翰英纳斯研究中心（John Innes Centre）和厄尔汉姆研究中心共同资助下担任研究员，同时建立表型创新实验室。2017年10月作为****引进到南京农业大学作物表型组学学交叉研究中心。2018年3月，联系协调中英七家单位共同建立中英植物表型组学联合研究中心。
主要学术成就：1）所领导的团队开展农作物表型组学研究、关键技术研发、以及相应的应用研究，包括开发和使用计算机视觉算法、机器学习和田间遥感等技术，进行田间农作物表型高通量监测与鉴定、重要基因与QTL定位，指导作物遗传改良与新品种选育等；2）领导开发了各类表型分析软硬件，包括基于Phenospex^TM（田间三维成像系统）的高通量分析软件、基于高性能计算机的表型分析平台（CropMonitor）、基于物联网的分布式表型监测系统（CropQuant）、基于机器学习的种子萌发和幼苗活力分析与筛选平台（SeedGerm）、基于无人机和轻型飞机图像数据分析获得的大规模作物信息与智慧农作决策的软件系统（AirSurf）等；3）所带领的团队与伯尔罕研究所（IBERS）、洛桑研究所（Rothamsted Research）、约翰英纳斯研究所（JIC）等开展基于室内和田间表型设施的农作物表型特征提取和深度图像分析等领域的研究；4）自2016年起，本人带领的团队还与先正达（Syngenta）、拜耳（Bayer Crop Science）、G’s Growers（英国第二大的种植公司）和英特尔等著名企业开展紧密的产学合作，并获得了上述企业直接研究经费的支持。
目前依托南京农业大学作物表型组学交叉研究中心和中英植物表型组学联合研究中心，正在组建一支中英联合研究团队。以我国重要农作物小麦和水稻为研究对象，自主开发基于高通量图像分析、计算机视觉、田间遥感技术和机器学习等学科的对农作物个体或群体高通量表型分析的关键技术。结合多组学的数据整合分析，推动植物表型组学（Plant Phenomics）在作物科学中的应用，提升我国农作物遗传育种、栽培管理和农业生产服务能力。自2011年至今，以生物信息学专家的身份参与完成多项交叉基础、应用研究项目。作为主要完成者在Nature，PNAS，Plant Cell，Nature Plants，Traffic和Plant Methods等国际期刊撰写发表学术论文17篇，专著章节2个，总影响因子超过115。定期为5家国际科学期刊审稿。获得英国与欧洲农业科技专利（CropQuant，PJP/81353GB1）1项、另一项专利（SeedGerm, GB**.9）进入英国专利局的审批程序。研究成果被超过15家英国和欧洲媒体报道或专访 。本人领导的实验室被评为东部英国最有潜力科研产业的提名（农业科技类，Eastern Daily Press）

所获项目基金
· 2018: Seed Fund, Co-PI – A GPU-accelerated deep-learning based Agri-Tech robotic system for CropQuant.
· 2018: ATCNN (Agri-Tech China: Newton Network+) focus award, PI – Identify key wheat growth stages based on large aerial images captured by UAVs and fixed-wing light aircrafts in the UK and China.
· 2017: Medical Research Council, Co-I – Phenome UK: UK crop phenotyping from sensors to knowledge.
· 2017: Bayer AG G4T focus award, PI – Develop image-based machine learning technologies to enable trait measurements of spikes per unit area and spikelet number and anther extrusion for hybrid wheat seed production breeding at Bayer.
· 2017: BBSRC Follow-on Pathfinder award, PI – CropQuant: Next-generation cost-effective crop monitoring system for breeding, crop research and digital agriculture.
· 2017: BBSRC Responsive mode award, Co-I – Genetic improvement of rice seed vigour for dry direct-seeded conditions.
· 2017: UK Science & Innovation Network, PI – Crop phenotyping innovations between the UK and US crop research community.
· 2017: Syngenta industrial collaboration, PI – A machine-learning based seed germination software solution for screening commercial seeds.
· 2016: BBSRC’s designing future wheat programme, the only named Chinese Co-PI.
· 2016: Industrial collaborative fund, PI – Applying latest financial analytics technology (First Derivative) to the establishment of predictive model assisted climate-smart agriculture.
· 2016: NRP Translational Fund, PI – SeedGerm: the next-generation phenotyping platform to quantify seed germination and seedling vigour.
· 2016: Eastern Agri-Tech Growth Initiative Grant, PI – CropQuant for precision agriculture.
· 2016: NRP Translational Fund, PI – CropQuant: the next generation crop monitoring workstation for precision agriculture.
· 2016: TGAC IDG, PI – Developing a novel high-throughput bioimage analysis pipeline to understand vegetable growth and crop yield.
· 2016: JIC/TGAC KEC Innovation grant, PI – A machine-learning based automated phenotyping platform for monitoring and quantifying seed germination frequency and seedling vigour for JIC and seed industry.
代表性论文列表 (^ 共同一作，* 通讯或共同通讯，粗体为本人实验室成员)
1. Zhou J*, Reynolds D, Le Cornu T, Websdale W, Gonzalez O, Lister C, Orford S, Laycock S, Stitt T, Clark M, Bevan M, Griffiths S*. (2017). CropQuant: The next-generation automated field phenotyping platform for breeding, crop research and digital agriculture. bioRxiv（https://www.biorxiv.org/content/early/2017/09/01/161547）
2. Alkhudaydi T, Zhou J*, De La lglesia B* (2018). Image segmentation for detecting in-field wheat spike regions using deep neural networks. IEEE ICIP 2018 (已接受).
3. Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linow M, Zhou J*, Tardieu F* (2018). Cost-efficient phenotyping – optimizing costs for different scenarios. Plant Science (已接受).
4. Alharbi N, Zhou J*, Wang WJ*, (2018). Automatic Counting of Wheat Spikelets From Time-lapse Wheat Plant Growth Images. IEEE Journal of Pattern Analysis and Applications, Pattern Recognition and Methods (已接受).
5. Watson A, Ghosh S, Williams M, Cuddy WS, Simmonds J, Rey M-D, Hatta MAM, Hinchliffe A, Steed A, Reynolds D, Adamski N, Breakspear A, Korolev A, Rayner T, Dixon LE, Riaz A, Martin W, Ryan M, Edwards D, Batley J, Raman H, Rogers C, Domoney C, Moore G, Harwood W, Nicholson P, Dieters MJ, DeLacy IH, Zhou J, Uauy C, Boden SA, Park RF, Wulff BBH, Hickey LT. (2018). Speed breeding: a powerful tool to accelerate crop research and breeding. Nature Plants, 4:23–29.

6. Zhou J*, Applegate C, Alonso AD, Reynolds D, Orford S, Mackiewicz M, Griffiths S, Penfield S, Pullen N (2017). Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat. Plant Methods, 13:117.
7. Faulkner C^, Zhou J^, Evrard A, Bourdais G, MacLean D, Häweker H, Garcia M, Bakal C, Eckes P, Robatzek S. (2017). An automated quantitative image analysis approach for identifying microtubule patterns. Traffic, 11(2): 109-117.
8. Bevan M. W., Uauy C., Wulff B. B. H., Zhou J., Krasileva K., Clark M. D. (2017). Genomic innovation for crop improvement. Nature, 543:346–354.
9. Meteignier L. V.^, Zhou J^, Cohen M., Bhattacharjee S., Goretty M., Chan C., Robatzek S., Moffett P. (2016). NB-LRR signaling induces translational repression of viral transcripts and the formation of RNA processing bodies through mechanisms differing from those activated by UV stress and RNAi. Journal of Experimental Botany, 67(4).
10. CRK Consortium (2015). Large-scale phenomics identifies primary and fine-tuning roles for CRKs in responses related to oxidative stress. PLOS Genetics: 11(7).
11. Beck M, Zhou J, Faulkner C, Robatzek S (2014). High-throughput imaging of plant immune responses, Plant-Pathogen Interactions: 27(11): 67-80.
12. Fitzgibbon, J., Beck, M., Zhou J, Faulkner, C., Robatzek, S., and Oparka, K. (2013). A developmental framework for complex plasmodesmata formation revealed by large-scale imaging of the Arabidopsis leaf epidermis. The Plant Cell: 25: 57–70.

13. Zhou J, Spallek, T., Faulkner, C., and Robatzek, S. (2013). CalloseMeasurer: a novel software solution to measure callose deposition and callose patterns. Plant methods: 8: 49.
14. Beck, M., Zhou J, Faulkner, C., MacLean, D., and Robatzek, S. (2012). Spatio-temporal cellular dynamics of the Arabidopsis flagellin receptor reveal activation status-dependent endosomal sorting. The Plant Cell: 24: 4205–19.