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  • 教育及工作經(jīng)歷:2001年于中國科學(xué)技術(shù)大學(xué)自動化系和管理科學(xué)系獲工學(xué)學(xué)士和管理學(xué)第二學(xué)士學(xué)位,2006年于中國科學(xué)技術(shù)大學(xué)自動化系獲工學(xué)博士學(xué)位,2006年7月起加入南京大學(xué)工程管理學(xué)院任教,曾于2012年至2013年在美國普林斯頓大學(xué)做訪問學(xué)者,并多次赴澳大利亞新南威爾士大學(xué)、香港城市大學(xué)等做短期訪問研究。目前為南京大學(xué)工程管理學(xué)院教授、博士生導(dǎo)師、副院長、機器學(xué)習(xí)與智能決策中心主任。


    學(xué)術(shù)研究:研究方向為機器學(xué)習(xí)與隨機優(yōu)化,及其在復(fù)雜系統(tǒng)管理與控制中的應(yīng)用,包括:強化學(xué)習(xí)、機器人與智能無人系統(tǒng)、量子控制與量子人工智能等。已在IEEE Transactions系列、中國科學(xué)、自動化學(xué)報等期刊及ICML、NeurIPS等一流會議發(fā)表學(xué)術(shù)論文100余篇,申請國家發(fā)明專利20余項,2008年入選南京大學(xué)青年骨干教師培養(yǎng)計劃,2013年入選江蘇省“333高層次人才培養(yǎng)工程”中青年科學(xué)技術(shù)帶頭人。


    教學(xué)及人才培養(yǎng):先后承擔(dān)《自動控制原理》、《自動化導(dǎo)論》、《智能控制與機器人學(xué)》、《工程矩陣論》、《機器感知與智能控制》等本科生、研究生課程;近年來指導(dǎo)本科生、研究生獲得國際及國家級競賽獎項20余項,先后共指導(dǎo)研究生50人(其中3人次獲南京大學(xué)棟梁特等獎、9人次獲國家獎學(xué)金、1人次獲評南京大學(xué)學(xué)生年度人物)。

  • 機器學(xué)習(xí)與隨機優(yōu)化,及其在復(fù)雜系統(tǒng)管理與控制中的應(yīng)用,包括:強化學(xué)習(xí)、機器人及智能無人系統(tǒng)、量子控制與量子人工智能。



  • 2023年南京大學(xué)“師德先進”青年教師獎

    2023年首屆“興智杯”全國人工智能創(chuàng)新應(yīng)用大賽三等獎:“AI機器人化學(xué)家領(lǐng)航者”

    2022年度中國指揮與控制學(xué)會科學(xué)技術(shù)進步獎二等獎:“智能博弈推演關(guān)鍵技術(shù)及其應(yīng)用”

    2021年第七屆中國國際“互聯(lián)網(wǎng)+”大學(xué)生創(chuàng)新創(chuàng)業(yè)大賽南京大學(xué)優(yōu)秀指導(dǎo)教師

    2020年度中國仿真學(xué)會2020年優(yōu)秀科技工作者

    2019年度南京大學(xué)鄭鋼基金—學(xué)業(yè)導(dǎo)師優(yōu)秀示范獎

    2019年度南京大學(xué)“雙創(chuàng)之星”教師

    2018年度南京大學(xué)社會實踐優(yōu)秀指導(dǎo)教師

    2017年度江蘇省教學(xué)成果獎(高等教育類)二等獎:“以優(yōu)化決策能力為特色的復(fù)合創(chuàng)新型管理人才培養(yǎng)——模式與實踐”

    2016年度中國指揮與控制學(xué)會科學(xué)技術(shù)進步獎二等獎:“融合不同智能特性及能力的指揮與控制決策問題求解理論與技術(shù)”

    2014年度南京大學(xué)工程管理學(xué)院協(xié)鑫獎教金

    2013年度高等學(xué)校科學(xué)研究優(yōu)秀成果獎(科學(xué)技術(shù))自然科學(xué)獎二等獎:“機器學(xué)習(xí)理論及其在復(fù)雜系統(tǒng)分析與控制中的應(yīng)用”

    2011年度江蘇省高教學(xué)會第十次高等教育科學(xué)研究成果獎三等獎:“自動化本科專業(yè)立體化教學(xué)改革與實踐”

    2010年度首批南京大學(xué)石林集團獎教金;

    2008年度南京大學(xué)優(yōu)秀教師;

    2008年度南京大學(xué)優(yōu)秀多媒體教學(xué)課件二等獎。




  • [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] 基于自適應(yīng)噪聲的最大熵進化強化學(xué)習(xí)方法. 自動化學(xué)報, 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.


  • 《自動化導(dǎo)論》(第三版,周獻中、陳春林 主編),科學(xué)出版社,北京,2022年8月.

  • 先后主持國家自然科學(xué)基金項目以及其他重大基礎(chǔ)課題、省級專項課題、企業(yè)委托項目等近20項。

  • 南京大學(xué)青年學(xué)者聯(lián)誼會理事、秘書長

    中國自動化學(xué)會系統(tǒng)仿真專委會副主任委員

    江蘇省系統(tǒng)工程學(xué)會副理事長

    中國自動化學(xué)會ADPRL專委會委員

    中國自動化學(xué)會青年工作委員會委員

    中國人工智能學(xué)會機器學(xué)習(xí)專委會委員

    中國指揮與控制學(xué)會青年工作委員會委員

    江蘇省自動化學(xué)會理事


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

  • 江蘇省第4期“333工程”第三層次(中青年科學(xué)技術(shù)帶頭人)

    南京大學(xué)第3屆“雙創(chuàng)之星(教師)”

    中國仿真學(xué)會優(yōu)秀科技工作者(2020年度)

  • 申請國家發(fā)明專利25項,其中已獲授權(quán)12項。

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