1990年9月至1995年7月 在清華大學(xué)自動(dòng)化系過程控制專業(yè)學(xué)習(xí)，獲學(xué)士學(xué)位。

1995年9月至1999年10月 在清華大學(xué)自動(dòng)化系控制理論與控制工程專業(yè)學(xué)習(xí)，獲博士學(xué)位。

1999年10月至2002年11月 清華大學(xué)自動(dòng)化系過程控制工程研究所 講師。

2002年12月至2008年11月 清華大學(xué)自動(dòng)化系過程控制工程研究所 副教授。

2007年1月至2008年1月 美國密西根大學(xué)工業(yè)與運(yùn)作工程系 訪問學(xué)者。

2008年12月至今 清華大學(xué)自動(dòng)化系過程控制工程研究所 教授 博士生導(dǎo)師。

[1] 智能優(yōu)化算法及其應(yīng)用 (本科生課程)。

[2] 自動(dòng)控制原理 (本科生課程) [北京市精品課程] [國家精品課程]。

[3] 生產(chǎn)調(diào)度及其智能優(yōu)化 (研究生課程)。

[4]人工神經(jīng)網(wǎng)絡(luò)(研究生課程)。

[5] 文獻(xiàn)檢索與論文寫作 (工程碩士課程)。

智能優(yōu)化理論、方法與應(yīng)用。

復(fù)雜生產(chǎn)過程建模、優(yōu)化與調(diào)度。

[1]中國仿真學(xué)會(huì)智能優(yōu)化與調(diào)度專委會(huì)副主任委員

[2] 中國自動(dòng)化學(xué)會(huì)控制理論與應(yīng)用專委會(huì)委員

[3]中國自動(dòng)化學(xué)會(huì)過程控制專委會(huì)委員

[4] 中國自動(dòng)化學(xué)會(huì)能源互聯(lián)網(wǎng)專委會(huì)常務(wù)理事

[5] 中國運(yùn)籌學(xué)會(huì)排序?qū)Ｎ瘯?huì)常務(wù)理事

[6] 中國運(yùn)籌學(xué)會(huì)智能工業(yè)數(shù)據(jù)解析與優(yōu)化專委會(huì)常務(wù)理事

[7] 中國人工智能學(xué)會(huì)智能優(yōu)化專委會(huì)常務(wù)理事

[8] 北京市自動(dòng)化學(xué)會(huì)常務(wù)理事

[9] International Journal of Automation and Control主編

[10] IEEE Transactions on Evolutionary Computation副編輯

[11] Swarm and Evolutionary Computation副編輯

[12] Int J of Applied and Computational Mathematics副編輯

[13] Int J of Artificial Intelligence and Soft Computing編委

[14] Journal of Optimization編委

[15] Memetic Computing編委

[16] 《控制理論與應(yīng)用》編委

[17] 《控制與決策》編委

[18] 《控制工程》編委

[19] 《系統(tǒng)工程與電子技術(shù)》編委

[1]國家杰出青年科學(xué)基金(61525304)：智能優(yōu)化調(diào)度理論與方法。(負(fù)責(zé)人) (2016.1~2020.12)

[2] 國家自然科學(xué)基金項(xiàng)目(61873328)：分布式生產(chǎn)調(diào)度的協(xié)同群智能優(yōu)化理論與方法。(負(fù)責(zé)人) (2019.1~2022.12)

[3]國家自然科學(xué)基金項(xiàng)目(61174189)：復(fù)雜資源受限項(xiàng)目調(diào)度問題及其混合智能算法研究。(負(fù)責(zé)人) (2012.1~2015.12)

[4] 國家自然科學(xué)基金項(xiàng)目(70871065)：基于學(xué)習(xí)機(jī)制的群智能調(diào)度理論與方法研究。(負(fù)責(zé)人) (2009.1~2011.12)

[5] 國家自然科學(xué)基金項(xiàng)目(60774082)：復(fù)雜生產(chǎn)系統(tǒng)基于差分進(jìn)化和量子進(jìn)化的優(yōu)化調(diào)度理論與方法。(負(fù)責(zé)人) (2008.1~2010.12)

[6] 國家自然科學(xué)基金項(xiàng)目(60374060)：復(fù)雜生產(chǎn)系統(tǒng)的智能仿真優(yōu)化理論與方法研究。(負(fù)責(zé)人) (2004.1~2006.12)

[7] 國家自然科學(xué)基金項(xiàng)目(60204008)：復(fù)雜系統(tǒng)基于計(jì)算智能的混合優(yōu)化理論與方法。(負(fù)責(zé)人) (2003.1~2005.12)

[8] 國家自然科學(xué)基金重點(diǎn)項(xiàng)目(60834004)：復(fù)雜芯片制造過程實(shí)時(shí)調(diào)度與優(yōu)化控制理論和算法研究及應(yīng)用。(骨干) (2009.1~2012.12)

[9] 教育部新世紀(jì)優(yōu)秀人才支持計(jì)劃(NCET-10-0505)。(負(fù)責(zé)人) (2010.1~2012.12)

[10] 高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金(20130002110057)：基于協(xié)同分布估計(jì)算法的分布式車間調(diào)度研究。(負(fù)責(zé)人) (2014.1~2016.12)

[11] 高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金(20100002110014)：基于新型混合群智能的資源約束項(xiàng)目調(diào)度研究。(負(fù)責(zé)人) (2011.1~2013.12)

[12] 北京市科技新星計(jì)劃(2004A41)：混合智能優(yōu)化調(diào)度理論與算法研究。(負(fù)責(zé)人) (2004.7~2007.7)

[13] 教育部留學(xué)回國啟動(dòng)基金：基于混合差分進(jìn)化的優(yōu)化調(diào)度研究。(負(fù)責(zé)人) (2009.1~2010.12)

[14] 國家重點(diǎn)研發(fā)計(jì)劃(2016YFB0901900)：能源互聯(lián)網(wǎng)的規(guī)劃、運(yùn)行與交易基礎(chǔ)理論。(課題一負(fù)責(zé)人) (2016.07~2020.06)

[15] 973計(jì)劃課題(2013CB329503)：面向腦信息編解碼的機(jī)器學(xué)習(xí)方法。(骨干) (2013.01~2017.12)

[16] 973計(jì)劃課題(2009CB320602)：復(fù)雜生產(chǎn)制造全流程基于數(shù)據(jù)和知識(shí)的實(shí)時(shí)智能運(yùn)行優(yōu)化理論和方法研究。(骨干) (2011.01~2013.08)

[17] 973計(jì)劃課題(2002CB312203)：復(fù)雜生產(chǎn)制造過程實(shí)時(shí)、智能控制與優(yōu)化理論和方法研究。(骨干) (2002.12~2008.5)

[18] 863計(jì)劃項(xiàng)目(2007AA04Z155)：流程工業(yè)企業(yè)生產(chǎn)過程的智能計(jì)劃與動(dòng)態(tài)優(yōu)化調(diào)度技術(shù)。(副組長) (2008.1~2009.12)

[19] 國家科技重大專項(xiàng)(2011ZX02504-008)：集成電路生產(chǎn)線智能調(diào)度與質(zhì)量優(yōu)化控制技術(shù)研究。(骨干) (2011.1~2013.12)

[1] 王凌, 王圣堯, 方晨. 分布估計(jì)調(diào)度算法. 北京: 清華大學(xué)出版社, 2017

[2] 王凌, 錢斌. 混合差分進(jìn)化與調(diào)度算法. 北京: 清華大學(xué)出版社, 2012.

[3] 王凌, 劉波. 微粒群優(yōu)化與調(diào)度算法. 北京: 清華大學(xué)出版社, 2008.

[4] 王京春, 王凌, 金以慧 (譯). 過程的動(dòng)態(tài)特性與控制. 北京: 電子工業(yè)出版社, 2006.

[5] 王凌. 車間調(diào)度及其遺傳算法. 北京: 清華大學(xué)&Springer出版社, 2003.

[6] 王凌. 智能優(yōu)化算法及其應(yīng)用. 北京: 清華大學(xué)&Springer出版社, 2001.

[7] Wang JJ, Wang L. A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (Regular Paper).

[8] Wu CG, Li W, Wang L, Zomaya AY. Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things. IEEE Transactions on Cloud Computing. (Regular Paper).

[9] Liao ZW, Gong WY, Yan XS, Wang L, Hu CY. Solving nonlinear equations system with dynamic repulsion-based evolutionary algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (Regular Paper).

[10] Gong WY, Wang Y, Cai ZH, Wang L. Finding multiple roots of nonlinear equation systems via a repulsion-based adaptive differential evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (Regular Paper).

[11] Chen HK, Tian Y, Pedrycz W, Wu GH, Wang R, Wang L. Hyperplane assisted evolutionary algorithm for many-objective optimization problems. IEEE Transactions on Cybernetics. (Regular Paper).

[12] Sun BQ, Wang L. A decomposition-based matheuristic for supply chain network design with assembly line balancing. Computers & Industrial Engineering.

[13] Wang JJ, Wang L. Decoding methods for the flow shop scheduling with peak power consumption constraints. International Journal of Production Research.

[14] Hu CY, Dai LG, Yan XS, Gong WY, Liu XB, Wang L. Modified NSGA-III for sensor placement in water distribution system. Information Sciences.

[15] Xiang S, Xing LN, Wang L, Zou K. Comprehensive learning pigeon-inspired optimization with tabu list. SCIENCE CHINA Information Sciences.

[16] Lei DM, Li M, Wang L. A two-phase meta-heuristic for multi-objective flexible job shop scheduling problem with total energy consumption threshold. IEEE Transactions on Cybernetics, 2019, 49(3): 1097-1109. (Regular Paper).

[17] Wang L, Lu JW. A memetic algorithm with competition for the capacitated green vehicle routing problem. IEEE/CAA Journal of Automatica Sinica, 2019, 6(2): 516-526.

[18] Jiang ED, Wang L. An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time. International Journal of Production Research, 2019, 57(6): 1756-1771.

[19] Zhang JW, Wang L, Xing LN. Large-scale medical examination scheduling technology based on intelligent optimization. Journal of Combinatorial Optimization, 2019, 37(1): 385-404.

[20] Zheng XL, Wang L. A collaborative multi-objective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(5): 790-800. (Regular Paper).

[21] Wang Y, Shi JM, Wang R, Liu Z, Wang L. Siting and sizing of fast charging stations in highway network with budget constraint. Applied Energy, 2018, 228: 1255-1271.

[22] Gong WY, Yan XS, Hu CY, Wang L, Gao L. Fast and accurate parameter extraction for different types of fuel cells with decomposition and nature-inspired optimization method. Energy Conversion and Management, 2018, 174: 913-921.

[23] Wang R, Lai SM, Wu GH, Xing LN, Wang L, Ishibuchi H. Multi-clustering via evolutionary multi-objective optimization. Information Sciences, 2018, 450: 128-140.

[24] Wu CG, Wang L. A multi-model estimation of distribution algorithm for energy efficient scheduling under cloud computing system. Journal of Parallel and Distributed Computing, 2018, 117: 63-72.

[25] Wang L, Zheng XL. A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem. Swarm and Evolutionary Computation, 2018, 38: 54-63.

[26] Gao KZ, Wang L, Luo JP, Jiang H, Sadollah A, Pan QK. Discrete harmony search algorithm for scheduling and rescheduling the re-processing problems in remanufacturing: A case study. Engineering Optimization, 2018, 50(6): 965-981.

[27] Wang R, Li GZ, Ming MJ, Wu GH, Wang L. An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system. Energy, 2017, 141: 2288-2299.

[28] Zheng HY, Wang L, Zheng XL. Teaching-learning-based optimization algorithm for multi-skill resource constrained project scheduling problem. Soft Computing, 2017, 21(6): 1537-1548.

[29] Deng J, Wang L. A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm and Evolutionary Computation, 2017, 32: 121-131.

[30] Zheng XL, Wang L. A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem. International Journal of Production Research, 2016, 54(18): 5554-5566.

[31] Tian MM, Jiang YH, Gao XY, Wang L, Huang DX. Plantwide scheduling model for the typical polyvinyl chloride production by calcium carbide method. Industrial & Engineering Chemistry Research, 2016, 55(21): 6161-6174.

[32] Zheng XL, Wang L. A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints. Expert Systems with Applications, 2016, 65: 28-39.

[33] Wang L, Wang SY, Zheng XL. A hybrid estimation of distribution algorithm for unrelated parallel machine scheduling with sequence-dependent setup times. IEEE/CAA Journal of Automatica Sinica, 2016, 3(3): 235-246.

[34] Shen JN, Wang L, Zheng HY. A modified teaching-learning-based optimization algorithm for bi-objective re-entrant hybrid flowshop scheduling. International Journal of Production Research, 2016, 54(12): 3622-3639.

[35] Deng J, Wang L, Wang SY, Zheng XL. A competitive memetic algorithm for the distributed two-stage assembly flow-shop scheduling problem. International Journal of Production Research, 2016, 54(12): 3561-3577.

[36] Wang SY, Wang L. An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46(1): 139-149. (Regular Paper).

[37] Shi L, Jiang YH, Wang L, Huang DX. Efficient Lagrangian decomposition approach for solving refinery production scheduling problems involving operational transitions of mode switching. Industrial & Engineering Chemistry Research, 2015, 54(25): 6508-6526.

[38] Zheng HY, Wang L. Reduction of carbon emissions and project makespan by a Pareto-based estimation of distribution algorithm. International Journal of Production Economics, 2015, 164: 421-432.

[39] Wang SY, Wang L, Liu M, Xu Y. An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem. International Journal of Computer Integrated Manufacturing, 2015, 28(3): 307-320.

[40] Zheng HY, Wang L. An effective teaching-learning-based optimization algorithm for RCPSP with ordinal interval numbers. International Journal of Production Research, 2015, 53(6): 1777-1790.

[41] Zhang X, Chen MY, Wang L, Peng ZH, Zhou DH. Connection-graph-based event-triggered output consensus in multi-agent systems with time-varying couplings. IET Control Theory and Applications, 2015, 9(1): 1-9.

[42] Shi L, Jiang YH, Wang L, Huang DX. Refinery production scheduling involving operational transitions of mode switching under predictive control system. Industrial & Engineering Chemistry Research, 2014, 53(19): 8155-8170.

[43] Pan QK, Wang L, Li JQ, Duan JH. A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimization. OMEGA-International Journal of Management Science, 2014, 45: 42-56.

[44] Wang L, Fang C, Mu CD, Liu M. A Pareto-archived estimation-of-distribution algorithm for multi-objective resource-constrained project scheduling problem. IEEE Transactions on Engineering Management, 2013, 60(3): 617-626. (Regular Paper).

[45] Wang SY, Wang L, Liu M, Xu Y. An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem. International Journal of Production Economics, 2013, 145(1): 387-396.

[46] Pan QK, Wang L, Sang HY, Li JQ, Liu M. A high performing memetic algorithm for the flowshop scheduling problem with blocking. IEEE Transactions on Automation Science and Engineering, 2013, 10(3): 741-756. (Regular Paper).

[47] Wang L, Zhou G, Xu Y, Liu M. A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem. International Journal of Production Research, 2013, 51(12): 3593-3608.

[48] Wang L, Wang SY, Liu M. A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem. International Journal of Production Research, 2013, 51(12): 3574-3592.

[49] Wang L, Zheng XL, Wang SY. A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowledge-Based Systems, 2013, 48: 17-23.

[50] Pan QK, Wang L, Mao K, Zhao JH, Zhang M. An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process. IEEE Transactions on Automation Science and Engineering, 2013, 10(2): 307-322. (Regular Paper).

[51] Wang L, Wang SY, Xu Y, Zhou G, Liu M. A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Computers & Industrial Engineering, 2012, 62(4): 917-926.

[52] Fang C, Wang L. An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Computers & Operations Research, 2012, 39(5): 890-901.

[53] Pan QK, Wang L. Effective heuristics for the blocking flowshop scheduling problem with makespan minimization. OMEGA-International Journal of Management Science, 2012, 40(2): 218-229.

[54] Wang L, Fang C. An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem. Computers & Operations Research, 2012, 39(2): 449-460.

[55] Wang L, Li LP. Fixed-structure H∞ controller synthesis based on differential evolution with level comparison. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 120-129. (Regular paper)

[56] Wang L, Fang C. An effective shuffled frog-leaping algorithm for multi-mode resource-constrained project scheduling problem. Information Sciences, 2011, 181(20): 4804-4822.

[57] Liu B, Wang L, Liu Y, Wang SY. A unified framework for population-based metaheuristics. Annals of Operations Research, 2011, 186(1): 231-262.

[58] Wang L, Pan QK, Tasgetiren MF. A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem. Computers & Industrial Engineering, 2011, 61(1): 76-83.

[59] Pan QK, Wang L, Gao L, Li WD. An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers. Information Sciences, 2011, 181(3): 668-685.

[60] Pan QK, Suganthan PN, Wang L, Gao L, Mallipeddi R. A differential evolution algorithm with self-adapting strategy and control parameters. Computers & Operations Research, 2011, 38(1): 394-408.

[61] Wang L, Li LP. An effective differential evolution with level comparison for constrained engineering design. Structural and Multidisciplinary Optimization, 2010, 41(6): 947-963.

[62] Liu B, Wang L, Liu Y, Qian B, Jin YH. An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes. Computers & Chemical Engineering, 2010, 34(4): 518-528.

[63] Wang L, Huang FZ. Parameter analysis based on stochastic model for differential evolution algorithm. Applied Mathematics and Computation, 2010, 217(7): 3263-3273.

[64] Wang L, Pan QK, Suganthan PN, Wang WH, Wang YM. A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Computers & Operations Research, 2010, 37(3): 509-520.

[65] Qian B, Wang L, Hu R, Huang DX, Wang X. A DE-based approach to no-wait flow-shop scheduling. Computers & Industrial Engineering, 2009, 57(3): 787-805.

[66] Qian B, Wang L, Huang DX, Wang X. Multi-objective no-wait flow-shop scheduling with a memetic algorithm based on differential evolution. Soft Computing, 2009, 13(8-9): 847-869.

[67] Pan QK, Wang L, Qian B. A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems. Computers & Operations Research, 2009, 36(8): 2498-2511.

[68] Qian B, Wang L, Huang DX, Wang X. An effective hybrid DE-based algorithm for flow shop scheduling with limited buffers. International Journal of Production Research, 2009, 47(1): 1-24.

[69] Qian B, Wang L, Huang DX, Wang WL, Wang X. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Computers & Operations Research, 2009, 36(1): 209-233.

[70] Li BB, Wang L, Liu B. An effective PSO-based hybrid algorithm for multi-objective permutation flow shop scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, 2008, 38(4): 818-831. (Regular paper)

[71] Liu B, Wang L, Jin YH. An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers. Computers & Operations Research, 2008, 35(9): 2791-2806.

[72] Li BB, Wang L. A hybrid quantum-inspired genetic algorithm for multi-objective flow shop scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 2007, 37(3): 576-591. (Regular paper).

[73] Liu B, Wang L, Jin YH. An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 2007, 37(1): 18-27. (Regular paper). (ESI)

[74] He Q, Wang L. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 2007, 20(1): 89-99. (ESI)

[75] Wang L, Zhang L, Zheng DZ. An effective hybrid genetic algorithm for flow shop scheduling with limited buffers. Computers & Operations Research, 2006, 33(10): 2960-2971.

[76] Liu B, Wang L, Jin YH, Tang F, Huang DX. Improved particle swarm optimization combined with chaos. Chaos, Solitons and Fractals, 2005, 25(5): 1261-1271. (ESI)

[77] Wang L, Zheng DZ. An effective hybrid heuristic for flow shop scheduling. International Journal of Advanced Manufacturing Technology, 2003, 21(1): 38-44.

[78] Jiang YH, Wang L, Jin YH. Bottleneck analysis for network flow model. Advances in Engineering Software, 2003, 34(10): 641-651.

[79] Zhou T, Wang L, Sun ZS. Closed-loop model set validation under a stochastic framework. Automatica, 2002, 38(9): 1449-1461.

[80] Wang L, Zheng DZ. An effective hybrid optimization strategy for job-shop scheduling problems. Computers & Operations Research, 2001, 28(6): 585-596.

[1]2015年度國家杰出青年科學(xué)基金

[2]2009年度教育部新世紀(jì)優(yōu)秀人才支持計(jì)劃

[3]2009年度清華大學(xué)學(xué)術(shù)新人獎(jiǎng)

[4]2004年度北京市科技新星

[5]2010年度Scopus青年科學(xué)之星新人獎(jiǎng)

[6]2016年度中國自動(dòng)化學(xué)會(huì)青年科學(xué)家獎(jiǎng)

[7]2014年度國家自然科學(xué)獎(jiǎng)二等獎(jiǎng)

[8]2017年度云南省自然科學(xué)獎(jiǎng)三等獎(jiǎng)

[9]2011年度中國電子學(xué)會(huì)電子信息科學(xué)技術(shù)獎(jiǎng)二等獎(jiǎng)

[10]2008年度北京市科學(xué)技術(shù)獎(jiǎng)三等獎(jiǎng)

[11]2007年度高等學(xué)校科學(xué)技術(shù)獎(jiǎng)自然科學(xué)獎(jiǎng)二等獎(jiǎng)

[12]2003年度教育部提名國家自然科學(xué)一等獎(jiǎng)

[13]2017年度《控制與決策》優(yōu)秀論文獎(jiǎng)

[14]2016年度《控制理論與應(yīng)用》優(yōu)秀論文獎(jiǎng)

[15]2014年度《自動(dòng)化學(xué)報(bào)》優(yōu)秀論文獎(jiǎng)

[16]領(lǐng)跑者5000, 中國精品科技期刊頂尖學(xué)術(shù)論文, 證書編號(hào)(7597): S026201203014

[17]領(lǐng)跑者5000, 中國精品科技期刊頂尖學(xué)術(shù)論文, 證書編號(hào)(10418): R060201402004

[18]2005-2010 Engineering Applications of Artificial Intelligence Top Cited Article Awarded by Elsevier

[19]IEEE ICIC杰出領(lǐng)導(dǎo)力獎(jiǎng), 2018

[20]IEEE國際智能計(jì)算會(huì)議最佳論文獎(jiǎng), ICICu20192018

[21]國際和聲搜索算法會(huì)議最佳論文獎(jiǎng), ICHSAu20192015

[22]中國過程控制年會(huì)Poster論文獎(jiǎng), CPCCu20192014

[23]高等計(jì)算智能與智能信息國際會(huì)議最佳論文獎(jiǎng), IWACIIIu20192013

[24]IEEE國際智能計(jì)算會(huì)議最佳論文獎(jiǎng), ICICu20192011

[25]Finalist for Zhang Si-Ying Outstanding Youth Paper Award, CCDCu20192010

[26]IET咨詢與控制技術(shù)國際會(huì)議優(yōu)秀論文, ICTu20192006

[27]IEEE機(jī)器學(xué)習(xí)和控制論國際會(huì)議優(yōu)秀論文獎(jiǎng), ICMLCu20192002

[28]中國控制與決策年會(huì)優(yōu)秀論文, CCDCu20192004

[29]清華大學(xué)優(yōu)秀博士論文一等獎(jiǎng)

[30]清華大學(xué)優(yōu)秀教材二等獎(jiǎng) (2004, 2008, 2012, 2016)

[31]清華大學(xué)第14屆良師益友 (2014)

[32]清華大學(xué)優(yōu)秀班主任一等獎(jiǎng) (2004, 2005)