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刘晨亮，中南大学自动化学院讲师。2024年6月获中南大学控制科学与工程专业博士学位（导师：王雅琳教授），2023年1月至2024年1月期间受国家留学基金委资助，在新加坡南洋理工大学进行联合培养。作为技术骨干参与科技部国家重点研发计划项目、国家自然科学基金重大研究计划重点项目等。

主要研究方向：人工智能与深度学习、工业大数据分析与建模、工业过程智能优化决策、智能制造与工业互联网等。

以第一/通讯作者在IEEE/CAA Journal of Automatica Sinica、IEEE Transactions on Industrial Informatics、IEEE Transactions on Industrial Electronics、IEEE Transactions on Cybernetics、IEEE Transactions on Knowledge and Data Engineering等自动化领域国际权威期刊发表SCI论文20余篇，其中ESI 1％高被引论文1篇，谷歌学术引用700余次，担任Nature Communications、IEEE TASE、IEEE TIM、EAAI、CEP等国际期刊审稿人。

曾荣获湖南省优秀毕业生、湖南省“芙蓉学子”学术科研奖、中南大学优秀毕业生、中南大学大学生年度人物等奖励。

“工业智能感知与优化调控”课题组招收博士（每年1-2个指标）和硕士研究生（每年4-6个指标），及部分学有余力的优秀本科生！

相关学科和研究领域包括但不限于：自动化、控制、计算机、数学、通信、工业互联网、人工智能、大数据分析等。

课题组氛围融洽，欢迎有意向从事相关研究工作的同学加入课题组！

课题组负责人：王雅琳老师，教授/博士生导师

课题组联系人：刘晨亮，联系邮箱：lcliang@csu.edu.cn

【代表性论文】

[1] Koopman-constrained hierarchical deep state space model for industrial quality prediction via cloud-edge collaborative framework[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025, 55(2): 1137-1150.

[2] Data-driven two-stage robust optimization allocation and loading for salt lake chemical enterprise products under demand uncertainty[J]. IEEE/CAA Journal of Automatica Sinica, 2025, 12(5): 989-1003.

[3] Attribution-aided nonlinear granger causality discovery method and its industrial application[J]. IEEE Transactions on Industrial Informatics, 2025.

[4] Interpretable switching deep markov model for industrial process monitoring via cloud-edge collaborative framework[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3501614.

[5] Two-stage hierarchical decision-making strategy for railway transportation freight stowage based on cooperative embedding optimization [J]. IEEE Transactions on Intelligent Vehicles. 2024.

[6] Multiscale feature fusion and semi-supervised temporal-spatial learning for performance monitoring in the flotation industrial process[J]. IEEE Transactions on Cybernetics, 2024, 54(2): 974-987.

[7] Learning deep multimanifold structure feature representation for quality prediction with an industrial application[J]. IEEE Transactions on Industrial Informatics, 2022, 18(9): 5849-5858.

[8] Operating condition recognition of industrial flotation processes using visual and acoustic bimodal autoencoder with manifold learning[J]. IEEE Transactions on Industrial Informatics, 2024, 20(5): 7428-7439.

[9] Data mode related interpretable transformer network for predictive modeling and key sample analysis in industrial processes[J]. IEEE Transactions on Industrial Informatics, 2023, 19(9): 9325-9336.

[10] Adaptive attention-driven manifold regularization for deep learning networks: Industrial predictive modeling applications and beyond [J]. IEEE Transactions on Industrial Electronics, 2024, 71(10): 13439-13449.

[11] Multimodal data-driven reinforcement learning for operational decision-making in industrial processes[J]. IEEE/CAA Journal of Automatica Sinica, 2024, 11(1): 252-254.

[12] Scope-free global multi-condition-aware industrial missing data imputation framework via diffusion transformer [J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(11): 6977-6988.

[13] Blackout missing data recovery in industrial time series based on masked-former hierarchical imputation framework[J]. IEEE Transactions on Automation Science and Engineering, 2024, 21(2): 1138-1150.

[14] Revolutionizing flotation process working condition identification based on froth audio[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 9513012.

[15] Deep nonlinear dynamic feature extraction for quality prediction based on spatiotemporal neighborhood preserving SAE[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 2516310.