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  • 科研(教学)奖励
  • 论文发表
  • 专著出版
  • 科研项目
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  • 教育及工作经历:2001年于中国科学技术大学自动化系和管理科学系获工学学士和管理学第二学士学位,2006年于中国科学技术大学自动化系获工学博士学位,2006年7月起加入必赢任教,曾于2012年至2013年在美国普林斯顿大学做访问学者,并多次赴澳大利亚新南威尔士大学、香港城市大学等做短期访问研究。目前为必赢教授、博士生导师、副经理、机器学习与智能决策中心主任。


    学术研究:研究方向为机器学习与随机优化,及其在复杂系统管理与控制中的应用,包括:强化学习、机器人与智能无人系统、量子控制与量子人工智能等。已在IEEE Transactions系列、中国科学、自动化学报等期刊及ICML、NeurIPS等一流会议发表学术论文100余篇,申请国家发明专利20余项,2008年入选必赢青年骨干教师培养计划,2013年入选江苏省“333高层次人才培养工程”中青年科学技术带头人。


    教学及人才培养:先后承担《自动控制原理》、《自动化导论》、《智能控制与机器人学》、《工程矩阵论》、《机器感知与智能控制》等本科生、研究生课程;近年来指导本科生、研究生获得国际及国家级竞赛奖项20余项,先后共指导研究生50人(其中3人次获必赢栋梁特等奖、9人次获国家奖学金、1人次获评必赢员工年度人物)。

  • 机器学习与随机优化,及其在复杂系统管理与控制中的应用,包括:强化学习、机器人及智能无人系统、量子控制与量子人工智能。



  • 2023年必赢“师德先进”青年教师奖

    2023年首届“兴智杯”全国人工智能创新应用大赛三等奖:“AI机器人化学家领航者”

    2022年度中国指挥与控制学会科学技术进步奖二等奖:“智能博弈推演关键技术及其应用”

    2021年第七届中国国际“互联网+”老员工创新创业大赛必赢优秀指导教师

    2020年度中国仿真学会2020年优秀科技工作者

    2019年度必赢郑钢基金—学业导师优秀示范奖

    2019年度必赢“双创之星”教师

    2018年度必赢社会实践优秀指导教师

    2017年度江苏省教学成果奖(高等教育类)二等奖:“以优化决策能力为特色的复合创新型管理人才培养——模式与实践”

    2016年度中国指挥与控制学会科学技术进步奖二等奖:“融合不同智能特性及能力的指挥与控制决策问题求解理论与技术”

    2014年度必赢协鑫奖教金

    2013年度高等学校科学研究优秀成果奖(科学技术)自然科学奖二等奖:“机器学习理论及其在复杂系统分析与控制中的应用”

    2011年度江苏省高教学会第十次高等教育科学研究成果奖三等奖:“自动化本科专业立体化教学改革与实践”

    2010年度首批必赢石林集团奖教金;

    2008年度必赢优秀教师;

    2008年度必赢优秀多媒体教学课件二等奖。




  • [62] Ess-InfoGAIL: Semi-supervised Imitation Learning from ImbalancedDemonstrations, Thirty-seventh Conference on Neural Information ProcessingSystems (NeurIPS 2023), New Orleans, Louisiana, United States of America,December 10-16, 2023.


    [61] Robust Spectral EmbeddingCompletion Based Incomplete Multi-view Clustering. The 31st ACM International Conference on Multimedia, Ottawa, Canada, October 29 – November 3, 2023.



    [60] Model-AwareContrastive Learning: Towards Escaping the Dilemmas. Proceedings of the 40thInternational Conference on Machine Learning(ICML 2023), PMLR 202:13774-13790, 23-29 July,2023, Honolulu, Hawaii, USA.


    [59] NA2Q: Neural Attention Additive Model for Interpretable Multi-AgentQ-Learning. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202:22539-22558, 23-29 July, 2023, Honolulu, Hawaii, USA.


    [58] Enhanced Tensor Low-Rankand Sparse Representation Recovery for Incomplete Multi-View Clustering. Proceedingsof the AAAI Conference on Artificial Intelligence (AAAI 2023), 37(9),11174-11182. February 7-14, 2023, Washington, DC, USA.


    [57] Joint Projection Learning and TensorDecomposition-Based Incomplete Multiview Clustering, IEEE Transactions on Neural Networks and Learning Systems, Doi:10.1109/TNNLS.2023.3306006, 2023.


    [56] Efficient Bayesian Policy Reuse with a Scalable ObservationModel in Deep Reinforcement Learning, IEEE Transactions on Neural Networks and Learning Systems, Doi: 10.1109/TNNLS.2023.3281604, 2023.


    [55] Hamiltonian Identification via Quantum EnsembleClassification, IEEE Transactions onNeural Networks and Learning Systems,Doi: 10.1109/TNNLS.2023.3258622, 2023.


    [54] Adaptive Marginalized Semantic Hashing for Unpaired Cross-ModalRetrieval, IEEE Transactions on Multimedia, 2023.


    [53] Low-Rank TensorRegularized Views Recovery for Incomplete Multiview Clustering, IEEE Transactions on Neural Networks andLearning Systems, Doi: 10.1109/TNNLS.2022.3232538, 2022.


    [52] Depthwise Convolution for Multi-Agent Communicationwith Enhanced Mean-Field Approximation, IEEE Transactions on Neural Networks and Learning Systems, Doi: 10.1109/TNNLS.2022.3230701, 2022.



    [51] Balancing Awareness Fast ChargingControl for Lithium-Ion Battery Pack Using Deep Reinforcement Learning, IEEE Transactions on Industrial Electronics,71(4): 3718-3727, April, 2024.


    [50] Instance Weighted IncrementalEvolution Strategies for Reinforcement Learning in Dynamic Environments, IEEE Transactions on Neural Networks and Learning Systems, 34(12): 9742-9756, December 2023.


    [49] A Dirichlet Process Mixture of Robust Task Models for Scalable LifelongReinforcement Learning. IEEE Transactions on Cybernetics, 53(12): 7509-7520, December 2023.


    [48] Two-step robust controldesign of quantum gates via differential evolution, Journal of The Franklin Institute, 360(17): 13972-13993, November, 2023.


    [47] Curriculum-based Deep Reinforcement Learning for Quantum Control, IEEE Transactions on Neural Networks and Learning Systems, 34(11):8852-8865, November 2023.


    [46] Magnetic Field-Based Reward Shaping for Goal-ConditionedReinforcement Learning, IEEE/CAA Journalof Automatica Sinica, 10(12): 2233-2247,2023.



    [45] Sparse spatial transformers for few-shot learning, SCIENCE CHINA Information Sciences, 2023, 66(11):210102.


    [44] Hierarchical Free Gait MotionPlanning for Hexapod Robots Using Deep Reinforcement Learning, IEEE Transactions on Industrial Informatics, Vol. 19, No. 11, November, 2023.


    [43] Extracting Decision Tree from TrainedDeep Reinforcement Learning in Traffic Signal Control, IEEE Transactionson Computational Social Systems, Vol. 10,No. 4, pp. 1997-2007, August, 2023.


    [42] Weakly-Supervised Enhanced Semantic-Aware Hashing for Cross-Modal Retrieval.IEEETransactions on Knowledge and Data Engineering, 35(6): 6475-6488, June 2023.


    [41] Multi-Level CascadeSparse Representation Learning for Small Data Classification, IEEE Transactionson Circuits and Systems for Video Technology, Vol. 33, No. 5, pp. 2451-2464, May, 2023.


    [40] Global Sensitivity Analysis forImpedance Spectrum Identification of Lithium-Ion Batteries Using Time-DomainResponse, IEEE Transactions on Industrial Electronics, Vol. 70, No. 4, pp. 3825-3835, April, 2023.


    [39] Adaptive Label Correlation Based Asymmetric Discrete Hashing forCross-Modal Retrieval, IEEE Transactions on Knowledge and Data Engineering, 35(2): 1185-1199, February, 2023.



    [38] 基于自适应噪声的最大熵进化强化学习方法. 自动化学报, 2023, 49(1):54−66, Doi: 10.16383/j.aas.c220103.


    [37] Perspective-corrected Spatial Referring Expression Generation for Human-RobotInteraction, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 52, No. 12, pp. 7654-7666, December, 2022.


    [36] On compression rate of quantum autoencoders: Control design,numerical and experimental realization, Automatica, 147:110659, 2022.


    [35] Shaping Visual Representations with Attributesfor Few-Shot Recognition, IEEE Signal Processing Letters, 29: 1397-1401, 2022.


    [34] Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots, the 30th International Joint Conference on Artificial Intelligence (IJCAI), Montreal-themed virtual reality, Pages 2381-2388, August 21-26, 2021.


    [33] Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction. IEEE/ASMETransactions on Mechatronics27(2): 846-857, 2022.


    [32] Lifelong Incremental Reinforcement Learning with Online Bayesian Inference. IEEETransactions on Neural Networks and Learning Systems, 33(8): 4003-4016, 2022.


    [31] Deep Reinforcement Learning with Quantum-inspired Experience Replay. IEEE Transactions on Cybernetics, 52(9): 9326-9338, 2022.


    [30] Locality-ConstrainedDiscriminative Matrix Regression for Robust Face Identification.IEEE Transactions on Neural Networks and Learning Systems, 33(3): 1254-1268, 2022.


    [29] Enhanced Group Sparse Regularized Nonconvex Regression for Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5): 2438-2452, 2022.


    [28] Pairwise Relations Oriented Discriminative Regression. IEEE Transactions on Circuits and Systems for Video Technology, 31(7): 2646-2660, 2021.


    [27] Nonnegative representation based discriminantprojection for face recognition. International Journal of Machine Learningand Cybernetics, 12: 733-745, 2021.


    [26] A multi-timescale framework for state monitoring and lifetimeprognosis of lithium-ion batteries. Energy, 229: 120684, 2021.


    [25] Realization of a quantum autoencoder forlossless compression of quantum data. Physical Review A, (102) 032412, 2020.


    [24] Incremental Reinforcement Learning in Continuous Spaces via Policy Relaxation and Importance Weighting. IEEE Transactions on Neural Networks and Learning Systems, 31(6): 1870-1883, 2020.


    [23] Learning-based Quantum Robust Control: Algorithm, Applications and Experiments. IEEE Transactions on Cybernetics, 50(8): 3581-3593, 2020.


    [22] Reinforcement Learning Based Optimal Sensor Placement for Spatiotemporal Modeling. IEEE Transactions on Cybernetics, 50(6): 2861-2871, 2020.


    [21] Incremental Reinforcement Learning with Prioritized Sweeping for Dynamic Environments. IEEE/ASME Transactions on Mechatronics, 24(2): 621-632, 2019.


    [20] Self-paced prioritized curriculum learning with coverage penalty in deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 29(6): 2216-2226, 2018.


    [19] Robust learning control design for quantum unitary transformations. IEEE Transactions on Cybernetics, 47(12): 4405-4417, 2017.


    [18] Quantum Learning Control Using Differential Evolution with Equally-mixed Strategies, Control Theory and Technology, 15(3): 226-241, 2017.


    [17] Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer. IEEE Transactions on Cybernetics, 47(5): 1238-1250, 2017.


    [16] Quantum Ensemble Classification: A Sampling-based Learning Control Approach. IEEE Transactions on Neural Networks and Learning Systems, 28(6): 1345-1359, 2017.


    [15] Learning robust pulses for generating universal quantum gates, Scientific Reports, 6: 36090, 2016.


    [14] Robust manipulation of superconducting qubits in the presence of fluctuations, Scientific Reports, 5: 7873, 2015.


    [13] Sampling-based learning control for quantum systems with uncertainties, IEEE Transactions on Control Systems Technology, 23(6): 2155-2166, 2015.


    [12] Fidelity-based Probabilistic Q-learning for Control of Quantum Systems. IEEE Transactions on Neural Networks and Learning Systems, 25(5): 920-933, 2014.


    [11] Sampling-based Learning Control of Inhomogeneous Quantum Ensembles. Physical Review A, 89: 023402, 2014.


    [10] Sampling-based Learning Control of Quantum Systems via Path Planning. IET Control Theory and Applications, 8(15): 1513-1522, 2014.


    [9] Further results on sampled-data design for robust control of a single qubit, International Journal of Control, 87(10): 2056-2064, 2014.


    [8] Control Design of Uncertain Quantum Systems with Fuzzy Estimators. IEEE Transactions on Fuzzy Systems, 20(5): 820-831, 2012.


    [7] Robust Quantum-Inspired Reinforcement Learning for Robot Navigation. IEEE-ASME Transactions on Mechatronics, 17(1): 86-97, 2012.


    [6] Probabilistic Fuzzy System for Uncertain Localization and Map-Building of Mobile Robots. IEEE Transactions on Instrumentation and Measurement, 61(6): 1546-1560, 2012.


    [5] Hybrid MDP Based Integrated Hierarchical Q-learning. Science China Information Sciences, 54(11): 2279-2294, 2011.


    [4] Incoherent Control of Quantum Systems with Wavefunction Controllable Subspaces via Quantum Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 38(4): 957-962, 2008.


    [3] Quantum Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 38(5): 1207-1220, 2008.


    [2] Hybrid Control for Robot Navigation - A Hierarchical Q-Learning Algorithm. IEEE Robotics & Automation Magazine, 15(2): 37-47, 2008.


    [1] Quantum Computation for Action Selection Using Reinforcement Learning. International Journal of Quantum Information, 4(6): 1071-1083, 2006.


  • 《自动化导论》(第三版,周献中、陈春林 主编),科学出版社,北京,2022年8月.

  • 先后主持国家自然科学基金项目以及其他重大基础课题、省级专项课题、企业委托项目等近20项。

  • 必赢青年学者联谊会理事、秘书长

    中国自动化学会系统仿真专委会副主任委员

    江苏省系统工程学会副理事长

    中国自动化学会ADPRL专委会委员

    中国自动化学会青年工作委员会委员

    中国人工智能学会机器学习专委会委员

    中国指挥与控制学会青年工作委员会委员

    江苏省自动化学会理事


    Chair of the Technical Committee on Quantum Cybernetics for IEEE SMC Society

  • 江苏省第4期“333工程”第三层次(中青年科学技术带头人)

    必赢第3届“双创之星(教师)”

    中国仿真学会优秀科技工作者(2020年度)

  • 申请国家发明专利25项,其中已获授权12项。