曹志兴

国家高层次青年人才计划入选者

入选《麻省理工科技评论》亚太区“35岁以下科技创新35人”

曹志兴，1990年1月出生，华东理工大学教授、博士生导师，国家高层次青年人才计划入选者，入选《麻省理工科技评论》亚太区“35岁以下科技创新35人”。2012年本科毕业于浙江大学控制科学与工程学系，2016年博士毕业于香港科技大学化学与生物分子工程学系，其先后于美国哈佛大学、英国爱丁堡大学担任博士后。2019年入职华东理工大学信息科学与工程学院，聘为教授。主持国家自然科学基金面上项目，担任中国自动化学会过程控制专委会委员和智能健康与生物信息专委会委员，国家自然科学基金委面上项目通讯评审专家、吴文俊人工智能科学技术奖通讯评审专家，以及Nature和Cell子刊审稿人。他致力于机器学习、医疗图像大数据和复杂生化反应智能建模的前沿研究，取得了一系列系统性的创新工作，多次以一作和通讯作者身份在Nature子刊、美国科学院院刊PNAS、Cell子刊Current Opinion in Biotechnology、IEEE Transactions on Automatic Control（长文）等著名期刊发表研究结果，获得香港特区政府博士奖学金、欧盟玛丽居里优秀项目书奖、2021年世界人工智能大会青年优秀论文提名奖、第32届中国过程控制会议张钟俊优秀论文奖等荣誉。两次入围加拿大英属哥伦比亚大学（UBC）和滑铁卢大学tenure-track教职。

基本信息：

        所属部门：自动化系

        办公地址：实验19楼1502

        办公电话：021-64253612

        电子邮箱：zcao@ecust.edu.cn

主讲课程：

        本科生课程《人工智能基础与应用》、博士生课程《智能理论与应用》

科研方向：

        机器学习、深度学习、智能图像处理、复杂生化反应智能建模

主要学术团体兼职：

        中国自动化学会过程控制专委会委员、中国自动化学会智能健康与生物信息专委会委员、国家自然科学基金面上项目通讯评审专家、吴文俊人工智能科学技术奖通讯评审专家、Nature/Cell子刊审稿人

科研项目：

[1]  国家自然科学基金面上项目，62073137，真核生物细胞及其受病毒感染后基因表达的随机动态建模研究，2021/01-2024/12，项目负责人。

代表性论文：

[1] Cao, Z.*, Grima, R.* (2018). Linear mapping approximation of gene regulatory networks with stochastic dynamics. Nature Communications, 9(1), 3305.

[2] Cao, Z., Grima, R.* (2020). Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells. Proceedings of the National Academy of Science of the United States of America, 117(9), 4682-4692.

[3] Jiang, Q., Fu, X., Yan, S., Li, R., Du, W., Cao, Z.*, Qian, F., Grima, R.* (2021). Neural network aided approximation and parameter inference of non-Markovian models of gene expression. Nature Communications, 12(1), 2618.

[4] Cao, Z., Grima, R.*(2019).Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.Journal of The Royal Society Interface 16 (153), 20180967.

[5] Cao, Z., Dürr, H. B., Ebenbauer, C., Allgöwer, F., Gao, F.(2017). Iterative learning and extremum seeking for repetitive time-varying mappings. IEEE Transactions on Automatic Control, 62(7), 3339-3353.

[6] Cao, Z., Zhang, R., Yang, Y., Lu, J., Gao, F.(2015). Discrete-time robust iterative learning Kalman filtering for repetitive processes. IEEE Transactions on Automatic Control 61 (1), 270-275.

[7] Cao, Z., Gondhalekar, R., Dassau, E., Doyle, F. J. (2017). Extremum seeking control for personalized zone adaptation in model predictive control for type 1 diabetes. IEEE Transactions on Biomedical Engineering, 65(8), 1859-1870.

[8] Cao, Z., Yang, Y.,Lu, J., Gao, F.* (2014).Constrained two dimensional recursive least squares model identification for batch processes.Journal of Process Control 24 (6), 871-879.

[9] Cao, Z., Lu, J., Zhang, R., Gao, F.* (2016). Iterative learning Kalman filter for repetitive processes. Journal of Process Control 46, 92-104.

[10] Cao, Z., Yang, Y., Yi, H., Gao, F.* (2016).Priori knowledge-based online batch-to-batch identification in a closed loop and an application to injection molding. Industrial & Engineering Chemistry Research 55 (32), 8818-8829.

[11] Cao, Z., Yang, Y., Lu, J., Gao, F.* (2015). Two-time-dimensional model predictive control of weld line positioning in bi-injection molding. Industrial & Engineering Chemistry Research 54 (17), 4795-4804.

[12] Cao, Z., Lu, J.*, Zhang, R.,Gao, F.* (2017). Online average-based system modelling method for batch process. Computers & Chemical Engineering, 108,128-138.

[13] Cao, Z., Zhang, R.,Lu, J., Gao, F.* (2016). Online identification for batch processes in closed loop incorporating priori controller knowledge. Computers & Chemical Engineering 90, 222-233. 

[14] Cao, Z., Zhang, R.,Lu, J.,Gao, F.* (2016). Two-time dimensional recursive system identification incorporating priori pole and zero knowledge. Journal of Process Control 39, 100-110.

[15] Cao, Z., Filatova, T., Oyarzún, DA., Grima, R.* (2020). A stochastic model of gene expression with polymerase recruitment and pause release. Biophysical Journal 119 (5), 1002-1014. 

[16] Lu, J., Cao, Z.*, Zhang, R., Gao, F. (2017). Nonlinear monotonically convergent iterative learning control for batch processes. IEEE Transactions on Industrial Electronics, 65(7), 5826-5836. 

[17] Lu, J., Cao, Z.*, Gao, F. (2018). Multipoint iterative learning model predictive control. IEEE Transactions on Industrial Electronics 66 (8), 6230-6240.

[18] Lu, J., Cao, Z.*, Hu, Q., Xu, Z., Du, W., Gao, F. (2020). Optimal iterative learning control for batch processes in the presence of time-varying dynamics. IEEE Transactions on Systems, Man, and Cybernetics: Systems. DOI: 10.1109/TSMC.2020.3031669. 

[19] Holehouse, J., Cao, Z., Grima, R.* (2020). Stochastic modeling of autoregulatory genetic feedback loops: A review and comparative study. Biophysical Journal 118 (7), 1517-1525. 

[20] Zhang, R.*,Cao, Z., Bo, C., Li, P., Gao, F. (2014). New PID controller design using extended nonminimal state space model based predictive functional control structure. Industrial & Engineering Chemistry Research 53 (8), 3283-3292.