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您现在的位置是: 刘驰

硕士生导师

姓名:刘驰
所在学科:计算机科学与技术
职称:教授、博士生导师
联系电话:13718763233
E-mail:chiliu@bit.edu.cn
通信地址:正规买球app下载软件楼316

个人信息

刘驰,教授、博士生导师、正规买球app下载副院长,智能信息技术北京市重点实验室主任,国家优秀青年科学基金获得者、国家重点研发计划首席科学家、中国电子学会会士、英国工程技术学会会士(Fellow of IET)、英国计算机学会会士(Fellow of British Computer Society)和英国皇家艺术学会会士(Fellow of Royal Society of Arts)。2006年本科于清华大学电子工程系,2010年博士毕业于英国帝国理工学院(Imperial College, UK)电气和电子工程系,后在德国电信研究总院(Deutsche Telekom Laboratories, Berlin)任博士后研究员,及在美国IBM TJ Watson研究中心和IBM中国研究院任研究主管。主要研究方向是:智能物联网技术。主持国家重点研发计划“工业软件”重点专项项目、国家自然科学基金青年/面上/优青/重点项目、国家重点研发计划课题、国防科技173计划技术领域基金、装备预研航天科技联合基金等;发表高水平论文百余篇,其中CCF-A类论文47篇、ESI高被引论文7篇,授权国内外发明专利29项,参与编著中英文书籍14本/节,谷歌引用5800余次,H index为34。获得ACM SigKDD 2021最佳论文亚军(Best Paper Runner-up Award)、ACM MobiCom 2021最佳社区论文亚军(Best Community Paper Runner-up Award),及省部级一等奖、二等奖、三等奖各1项。培养的多名学生获得北京市优秀本科毕业设计论文、中国电子学会优秀硕士论文等。

现任国家信息产业“十四五”规划专家顾问组成员、第四届全国信标委技术委员会委员、中国电子学会理事、中国计算机学会杰出会员,曾任中国工程院“十三五”战略性新兴领域高级咨询专家等。现任IEEE Transactions on Network Science and Engineering编辑(Associate Editor)并获最佳编辑奖(Excellent Editor Award)、SIGKDD、IJCAI、INFOCOM TPC(并获评2021年杰出程序委员,Distinguished TPC);曾任IEEE ICC 2020 Symposium on Next Generation Networking主席(Chair)。


科研方向

人工智能、物联网、大数据、边缘计算


代表性学术成果

代表性CCF-A类论文:

[JSAC]. C. H. Liu*, Z. Chen, J. Tang, J. Xu, C. Piao, "Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach," IEEE Journal of Selected Areas in Communications, Volume:3, Issue:9, Page(s): 2059-2070, 2018.

[JSAC]. C. H. Liu*, Z. Chen, Y. Zhan, "Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach," IEEE Journal of Selected Areas in Communications, Volume: 37, Issue: 6, Page(s): 1262 – 1276, June 2019.

[TKDE]. C. H. Liu*, J. Xu, J. Tang and J. Crowcroft, "Social-aware Sequential Modeling of User Interests: A Deep Learning Approach," IEEE Transactions on Knowledge and Data Engineering, Volume: 31, Issue: 11, Page(s): 2200 – 2212, Nov. 1 2019.

[TKDE]. C. H. Liu*, Y. Wang, C. Piao, Z. Dai, Y. Yuan, G. Wang, D. Wu, "Time-Aware Location Prediction by Convolutional Area-of-Interest Modeling and Memory-Augmented Attentive LSTM," IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2020.3005735, June 2020.

[TMC]. C. H. Liu, X. Ma, X. Gao and J. Tang*, "Distributed Energy-Efficient Multi-UAV Navigation for Long-Term Communication Coverage by Deep Reinforcement Learning," in IEEE Transactions on Mobile Computing, Volume: 19, Issue:6, Page(s): 1274-1285, JUNE 2020

[TMC]. C. H. Liu*, Z. Dai, Y. Zhao, J. Crowcroft, D. Wu and K. K. Leung, "Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning," in IEEE Transactions on Mobile Computing, Volume: 20, Issue: 1, Page(s): 130-146, January 2021.

[TMC]. Z. Dai, C. H. Liu*, R. Han, G. Wang, K. K. Leung, J. Tang, “Delay Sensitive Energy-Efficient UAV Crowdsensing by Deep Reinforcement Learning,” IEEE Transactions on Mobile Computing, DOI: 10.1109/TMC.2021.3113052, September 2021.

[TPAMI]. S. Li, C. H. Liu*, Q. Lin, Q. Wen, L. Su, G. Huang, Z. Ding, "Deep Residual Correction Network for Partial Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume:43, Issue: 7, Page(s): 2329-2344, July 2021.

[TPAMI]. S. Li, B. Xie, Q. Lin, C. H. Liu, G. Huang, and G. Wang, “Generalized Domain Conditioned Adaptation Network,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2021.3062644, 01 March 2021.

[TC]. R. Han, C. H. Liu*, S. Li, S. Wen, and X. Liu, “Accelerating Deep Learning Systems via Critical Set Identification and Model Compression,” IEEE Transactions on Computers, Volume: 69, Issue: 7, Page(s): 1059-1070, 1 July 2020.

[KDD]. H. Wang, C. H. Liu*, Z. Dai, J. Tang, G. Wang, “Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning,” in ACM SIGKDD 2021 , virtual, August 2021, Page(s): 3679–3687 (Best Paper Award - Runner Up).

[MOBICOM]. R. Han, Q. Zhang, C. H. Liu*, G. Wang, J. Tang, L. Y. Chen, “LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision,” in  ACM MOBICOM 2021 , virtual, October 2021, Page(s): 406–419.

[NeurIPS]. Fangrui Lv, Jian Liang, Kaixiong Gong, Shuang Li*, Chi Harold Liu, Han Li, Di Liu, Guoren Wang, “Pareto Domain Adaptation,” in  NeurIPS 2021 , virtual, Dec. 6-14, 2021.

[INFOCOM]. C. H. Liu, Z. Dai, H. Yang, J. Tang, “Multi-Task-Oriented Vehicular Crowdsensing: A Deep Learning Approach,” in  IEEE INFOCOM 2020, virtual, 6-9 July, Page(s): 1123-1132.

[INFOCOM]. C. H. Liu, C. Piao, J. Tang, “Energy-Efficient UAV Crowdsensing with Multiple Charging Stations by Deep Learning,” in  IEEE INFOCOM 2020 , virtual, 6-9 July, Page(s): 199-208.

[INFOCOM]. Z. Dai, H. Wang, C. H. Liu*, R. Han, J. Tang, G. Wang, “Mobile Crowdsensing for Data Freshness: A Deep Reinforcement Learning Approach,” in IEEE INFOCOM 2021 , virtual, 10-13 May, 2021, Page(s): 1-10.

[INFOCOM]. Z. Dai, C. H. Liu*, Y. Ye, R. Han, Y. Yuan, G. Wang, J. Tang, “AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning,” in IEEE INFOCOM 2022 , Virtual, 2-5 May, 2022.

[ICDE]. C. H. Liu, Y. Zhao, Y. Yuan, G. Wang, D. Wu, K. K. Leung, “Curiosity Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach,” in  IEEE ICDE 2020 , Dallas, USA, April 2020, Page(s):25-36.

[ICDE]. C. H. Liu, C. Piao, X. Ma, Y. Yuan, J. Tang, G. Wang, “Modeling Citywide Crowd Flows using Attentive Convolutional LSTM,” in  IEEE ICDE 2021 , virtual, April 19-23 2021, Page(s):217-228.

[ICDE]. Y. Wang, C. H. Liu*, C. Piao, Y. Yuan, R. Han, G. Wang, “Human-Drone Collaborative Spatial Crowdsourcing by Memory-Augmented and Multi-Agent Deep Reinforcement Learning,” in IEEE ICDE 2022 , virtual, May 9-12 2022.

[CVPR]. S. Li, M. Xie, K. Gong, C. H. Liu*, Y. Wang, W. Li, “Transferable Semantic Data Augmentation for Domain Adaptation,” in IEEE CVPR 2021 , virtual, June 19-25, 2021, Page(s):11516-11525. (oral presentation)

[AAAI]. Y. Zhao, K. Wu, Z. Xu, Z. Che, Q. Lu, J. Tang, C. H. Liu, “A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving,”  AAAI 2022 , Virtual, 22 Feb-1 March, 2022. (oral presentation)


代表性论著和教材:

[1] 刘驰(主编);韩锐、赵健鑫、马建(副主编),《物联网技术概论》第三版,机械工业出版社,2021年11月

[2] 韩锐、刘驰(著),《云边协同大数据系统技术与应用》,机械工业出版社,2022年2月

[3] 刘驰、王占健、马晓鑫、戴子彭等(编著),《深度强化学习:学术前沿与实践应用》,机械工业出版社,2020年4月

[4] 刘驰(主编)、符积高、徐闻春(编著),《Spark:原理、机制及应用》,机械工业出版社,2016年3月



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备注

1. 实验室每年约录取博士、硕士,本科保研学生若干,请抓紧发邮件到chiliu@bit.edu.cn联系。

2. 实验室拥有NVIDIA RTX、2080Ti、XP系列显卡60余块,CPU集群一个(15台服务器),GPU服务器5台,边缘计算设备8台,能充分满足人工智能与大数据的科研需求。

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4. 特别欢迎本校保研的同学提前来实验室,一起做高水平论文!

5. Last update March 2022


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