刘莹 女 教授 计算机科学与技术学院
电子邮件:yingliu@ucas.ac.cn
通信地址:中关村东路80号青年公寓6号楼215
邮政编码:100190
研究领域
数据挖掘,人工智能,并行计算
教育背景
学位
美国Northwestern University -- 硕士,2001
美国Northwestern University -- 博士 ,2005
工作经历
中国科学院大学计算机科学与技术学院,教授 2015.12 ~ 今
中国科学院大学计算机科学与技术学院,副教授 2007. 6 ~ 2015.11
美国内布拉斯加大学奥马哈分校,访问学者 2009.7 ~ 2009.9
中国科学院研究生院信息科学与工程学院, 讲师 2006.1 ~ 2007.5
美国Northwestern University,研究助理 2005.6 ~ 2005.11
教授课程
研究生课程
- 中国科学院大学计算机科学与技术学院
《高性能计算的新发展——基于图形处理器的并行计算及CUDA编程》 2009.2 ~ 今
《数据挖掘引论》 2006.9 ~ 今
《数据挖掘高级技术》 2007.2 ~ 2010.5
《数据挖掘算法讨论班》2009.9 ~ 今
- 中国科学院大学国际学院
《数据挖掘》 2013.9 ~ 今
- 中国科学院研究生院计算与通信工程学院
《数据挖掘》 2007.3 ~ 2007.5
培训课程
International Training Workshop on Open Science and SDGs,中国科学院,2022.09
安徽省大数据研究院,《基于GPU的并行计算及CUDA编程》,2020.08
广东省经济和信息化委大数据应用培训班,《大数据分析与挖掘》,2016.07-2016.08
交通部规划研究院,《大数据》,2016.05
中国科学院遥感与对地观测中心博士班课程 ,《大数据分析》,2015.11
- 中国科学院发展中国家科学大数据国际培训班,《大数据挖掘》,2015.5
- 中国人民大学新闻学院暑期工作坊,《大数据挖掘》,2014.7
- 大连理工大学,《基于图形处理器的并行计算及CUDA编程》,2014-2015
- 中国科学院大学中丹学院,《数据挖掘及其应用》,2013-2015
- 中国科学院大学管理学院MBA课程,《数据挖掘及其应用》,2011-2015
- 中国科学院国家天文台,《基于图形处理器的并行计算及CUDA编程》 2013.10
- 中国科学院软件研究所,《数据挖掘》,2010. 8
- 中国科学院,《基于图形处理器的并行计算及CUDA编程》,2009 - 2016
- 安捷伦科技有限公司(中国),《基于图形处理器的并行计算及CUDA编程》,2009. 6
本科生课程
中国科学院大学计算机科学与技术学院
《数据挖掘》2016 ~ 今
发表论文
专著
[1] 李国庆,刘莹,庞禄申,《地球科学中的大数据分析与挖掘算法手册》,人民邮电出版社,2018.
[2] 赵地,刘莹等,《强化学习》,机械工业出版社,2018
[3] 文跃然,刘莹等,《人力资源管理学习精要——基于人工智能的方法》,复旦大学出版社,ISBN:9787309136944/F.2463,2018。
[4] 刘莹,《大数据挖掘》,大数据时代的科研活动,科学出版社,ISBN:9787030401830,2014。
[5] Xiaojun Li, Yang Gao, Xinyu Ma,
Ying Liu, “Performance Evaluation of Fast Fourier Transform Application on
Heterogeneous Platforms”, GPU Solutions to Multi-scale Problems in Science and
Engineering. David A. Yuen, Long Wang, et al. ed., Springer, 2013. ISBN-10:
3642164048.
[6] Jianwei Li, Ying Liu, Wei-keng Liao, Alok Choudhary, “Parallel Data Mining Algorithms for Association Rules and Clustering”, Handbook of Parallel Computing: Models, Algorithms and Applications. Sanguthevar Rajasekaran and John Reif, ed., CRC Press, 2007.
期刊论文
[1] Tajuddeen Rabiu Gwadabe, Mohammed Al-hababi, Ying Liu, “SimGNN: Simplified Graph Neural Networks for Session-based Recommendation”, Applied Intelligence (accepted)
[2] Hao Gong, Ying Liu, Xiaoying Chen, Cheng Wang, “Scene Optimization of GPU‑based Back‑Projection Algorithm”, Journal of Supercomputing, https://doi.org/10.1007/s11227-022-04785-w. (SCI 3)
[3] Zhengyu Cui, Ying Liu, Wei Zhao, Cheng Wang, “Learning to Transfer Attention in Multi-level Features for Rotated Ship Detection”, Neural Computing and Applications, June, 2022, https://doi.org/10.1007/s00521-022-07491-z. (SCI 2)
[4] Tajuddeen Rabiu Gwadabe, Ying Liu, “Improving graph neural network for session-based recommendation system via non-sequential interactions”, Neurocomputing, Volume 468, Jan. 2022, pp. 111-122. (SCI 2, 计算机科学顶刊), h-index=110
[5] Tajuddeen Rabiu Gwadabe, Ying Liu, “IC-GAR: Item Co-occurrence Graph Augmented Session-based Recommendation”, Neural Computing and Applications, 34, pp. 7581–7596, 2022. (SCI 2)
[6] Jiaxu Leng, Ying Liu, Xinbo Gao, “Selective region enlargement network for fast object detection in high resolution images”, Neurocomputing, Vol. 462, Oct. 28, 2021, pp. 402-411. (SCI 2, 计算机科学顶刊), h-index=110
[7] Zhengyu Cui, Jiaxu Leng, Ying Liu, Tianlin Zhang, Pei Quan, Wei Zhao, “SKNet: Detecting Rotated Ships as Keypoints in Optical Remote Sensing Images”, IEEE Transactions on Geoscience and Remote Sensing (Print ISSN: 0196-2892, Online ISSN: 1558-0644), Feb. 2021, pp. 8826-8840, DOI: 10.1109/TGRS.2021.3053311. (SCI 1, 工程技术顶刊),h-index=216
[8] Jiaxu Leng, Ying Liu, “CrossNet: Detecting Objects as Crosses”, IEEE Transactions on Multimedia, Vol. 2, Feb. 18, 2021, pp. 861-875, (SCI 1, IF = 5.84, 计算机科学顶刊), h-index=108
[9] Jiaxu Leng, Ying Liu, “Context Augmentation for Object Detection”, June 2022, Applied Intelligence, 52(3), pp. 2621–2633, 2022. (SCI 3, IF = 3.3)
[10] Jiaxu Leng, Ying Liu, “Single-shot augmentation detector for object detection”, Neural Computing and Applications, Vol. 33, pp. 3583–3596, 2021, DOI: 10.1007/s00521-020-05202-0. (SCI 2, IF=4.66)
[11] Tianlin Zhang, Jiaxu Leng, Ying Liu, “Deep learning for drug–drug interaction extraction from the literature: a review”, Briefings in Bioinformatics, Nov. 2019, https://doi.org/10.1093/bib/bbz087. (SCI 1, IF = 9.1)
[12] Jiaxu Leng, Ying Liu, Shang Chen, “Context-aware Attention Network for Image Recognition”, Neural Computing and Applications, Vol. 31(12), June 2019, pp. 6295-6305. (SCI 2, IF=4.66)
[13] Jiaxu Leng, Ying Liu, Dawei Du, Tianlin Zhang, Pei Quan, “Robust Obstacle Detection and Recognition for Driver Assistance Systems”, IEEE Transactions on Intelligent Transportation Systems, Vol. 21(4), April 2020, pp. 1560-1571. (SCI 2, IF=5.74)
[14] Ying Liu, Hongyuan Cui, Renliang Zhao, “Fast Acquisition of Spread Spectrum Signals using Multiple GPUs”, IEEE Transactions on Aerospace and Electronic Systems, Vol 55(6), December 2019, pp. 3117-3125. (SCI 2, IF=2.8)
[15] Jiaxu Leng, Ying Liu, Real-Time RGB-D Visual Tracking With Scale Estimation and Occlusion Handling, IEEE Access 2018,DOI: 10.1109/ACCESS.2018.2831443. (SCI 2, IF=4.1)
[16] Jiaxu Leng, Ying Liu, “An enhanced SSD with feature fusion and visual reasoning for object detection”, Neural Computing and Applications, Vol. 31, 2019, pp. 6549-6558. (SCI 2, IF=4.66)
[17] Ying Liu, Guoyu Ou, “Real-Time Vehicular Traffic Violation Detection in Traffic Monitoring Stream”, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol. 22, (1) 2018, pp.53-64. (SCI 3, IF=2.57)
[18] Ying Liu, Haixin Zheng, Renliang Zhao, Liheng Jian, “Design and Evaluation of Multi-GPU enabled Multiple Symbol Detection Algorithm”, Journal of Supercomputing, Vol. 72(6), 2016.6, pp. 2111-2131, DOI 10.1007/s11227-015-1475-z.
[19] Zhongya Wang, Ying Liu, Jiajun Yang, Zheng Zheng, Kaichao Wu, “A Personalization-Oriented Academic Literature Recommendation Method”, Data Science Journal, 2015, DOI: http://doi.org/10.5334/dsj-2015-017.
[20] Hongyuan Cui, Jiajun Yang, Ying Liu, Zheng Zheng, Kaichao Wu, “Data Mining-based DNS Log Analysis”, Annals of Data Science, DOI: 10.1007/s40745-014-0023-7.
[21] Zhongya Wang, Ying Liu, Steve Chiu, “An Efficient Parallel Collaborative Filtering Algorithm on Multi-GPU Platform”, Journal of Supercomputing, Vol. 72(6), 2016.6, pp 2080-2094, DOI:10.1007/s11227-014-1333-4.
[22] Yuwei Wang, Yuanchun Zhou, Ying Liu, “A Grid-Based Clustering Algorithm for Wild Bird Distribution”, Frontiers of Computer Science, Vol. 7(4), 2013, pp. 475-485. DOI:10.1007/s11704-013-2223-2.
[23] Liheng Jian, Ying Liu, Weidong Yi, “CU-Simulator: A Parallel Scalable Simulation Platform for Radio Channel in Wireless Sensor Networks”, Ad Hoc & Sensor Wireless Networks, Vol.24, pp. 109-134.
[24] Liheng Jian, Cheng Wang, Ying Liu, Shenshen Liang, Weidong Yi, Yong Shi, "Parallel Data Mining on Graphics Processing Unit with Compute Unified Device Architecture (CUDA)", Journal of Supercomputing, Vol. 64(3), 2013, pp. 942-967.
[25] Ying Liu, Lingling Zhang, Yuehua Zhang, Yong Shi, " Bug Detection in Source Code in Process Monitoring", Journal of Computational Information System, Vol. 8(2), 2012, pp. 591-601.
[26] Ying Liu, Jianwei Li, Wei-keng Liao, Alok Choudhary, Yong Shi, “High Utility Itemsets Mining”, International Journal of Information Technology & Decision Making (IJITDM), Volume No.09, Issue No. 6, November 2010, pp. 905-934.
[27] Yuejin Zhang, Lingling Zhang, Ying Liu, Yong Shi, "Mining Intelligent Knowledge from a Two-phase Association Rules Mining", International Journal of Data Mining, Modeling and Management, Vol. 2, No. 4, 2010, pp. 403-418.
[28] Lian Duan, Lida Xu, Ying Liu and Jun Lee, “Cluster-based Outlier Detection”, Annals of Operations Research, Vol. 168, No. 1, April 2009, pp. 151-168.
[29] Guangli Nie, Lingling Zhang, Ying Liu, Xiuyu Zheng, Yong Shi, "Decision analysis of data mining project based on Bayesian risk", Journal of Expert Systems with Applications, Vol. 36, Issue 3, Part 1, April 2009, pp. 4589–4594.
[30] Cheng Wang, Ying Liu, Liheng Jian, Peng Zhang, "An Efficient Approach for Mining Web Content Sensitivity", International Journal of Knowledge and Web Intelligence, Vol. 1, Nos. 1/2, 2009, pp. 95-109.
[31] 魏千程, 吴开超, 刘莹. 基于迁移学习的信用评分预测,计算机系统应用, 2020, 29(11): 134-138.
[32] 冷佳旭, 刘莹,《基于深度学习的小目标检测与识别》,数据与计算发展前沿,2020,2(2).
[33] 陈源,王元钦,刘莹,《基于CORBA的扩展型事件服务模型设计 》,计算机应用, S1期, pp 138-140+143, 2011。
[34] 刘莹,菅立恒, 梁莘燊, 李小君,高洋,王琤,《基于CUDA架构的GPU的并行数据挖掘技术研究》,科研信息化技术与应用,2010年第4期,pp. 37-52。
特邀报告
《GPU推动AI》,2022GPU技术大会(中国)教育分论坛,线上,2022.09
GPU技术大会(中国)教育分论坛,苏州,2019.12
《基于上下文信息的道路通行时间预测算法》,大数据产学研高峰论坛,广州,2019.12
《基于深度学习的海面舰船检测与识别》,科学数据大会,贵阳,2019.08
《面向研究生的数据挖掘教学实践》,大数据技术大会教育分论坛,北京,2018.12
《交通大数据挖掘研究与应用》,大数据产学研高峰论坛,广州,2018.12
《基于异构并行计算的大数据分析与挖掘》,CSDN论坛,北京,2018.07
《基于深度学习的海面舰船识别》,科学数据大会,黑河,2018.07
《大数据挖掘在气象领域的应用》,国家气象局,2018.3
《Big Data Analysis and Applications》,Apec论坛,2017.12
《基于异构并行计算的大数据分析与挖掘》,大数据产学研高峰论坛,广州,2017.12
《Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning》,GPU/MIC, 贵阳,2017.8
《Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning》,科学数据大会,昆明,2017.08
《基于GPU的高性能数据挖掘》,华为北研所,2016.12。
《Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning》,大数据产学研高峰论坛,广州,2016.11.
《Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning》, Big Data Analytics Symposium, Beijing, 2016.11
《CUDA-Accelerated Acquisition of Spread Spectrum Signal in Satellite Communication》,GTC China,Beijing, China, 2016.09.
《基于深度学习的遥感图像舰船分类》,科学数据大会,2016.08.
《基于GPU的高性能数据挖掘》,中科院软件所,2016.04.
《CUDA-Accelerated Acquisition of Spread Spectrum Signal in Satellite Communication》,Big Data Analytics Forum,Beijing, China, 2015.10.
《CUDA-Accelerated Acquisition of Spread Spectrum Signal in Satellite Communication》,International Workshop on GPU/MIC Solutions to Multiscale Problems in Science and Engineering, 2015.8.
《An Efficient Parallel Collaborative Filtering Algorithm on Multi-GPU Platform》,GPU Technology Conference, USA, 2015.3.
《异构云中高性能计算中间件的研究》,第十届两岸三院信息技术与应用交流研讨会,2014.9。
《An Efficient Parallel Collaborative Filtering Algorithm on Multi-GPU Platform》,中国模板计算研讨会,2014.8。
《An Efficient Parallel Collaborative Filtering Algorithm on Multi-GPU Platform》, International Workshop on GPU/MIC Solutions to Multiscale Problems in Science and Engineering, 2014.8.
《Big Data Mining》,中国人民大学“大数据·建模方法与传播学研究”暑期工作坊,2014.7。
《Big Data Mining》,CODATA"发展中国家科学大数据国际培训班”,2014.6。
《大数据》,总参测绘局,2014.4.
《CUDA-Accelerated Satellite Communication Demodulation》,GPU Technology Conference, USA, 2014.3.
《异构云中高性能计算中间件的研究》,科学数据大会,2014.2。
《Enabling High Performance Data Intensive and Computation Intensive Applications in Heterogeneous Cloud》, 2013 Chinese-American-German E-Science and Cyberinfrastructure Workshop, 芝加哥,美国,2013.9.
《CUDA-enabled Multiple Symbol Detection Algorithm in PCM/FM Demodulation》, International Workshop on GPU Solutions to Multiscale Problems in Science and Engineering, 长春,2013.7.
《Sim-Midware: A Middleware to Enable High Performance ESL Simulation in Heterogeneous Cloud》,Agilent Technologies, 2013.7.
《大数据与大数据挖掘》,中国科学院计算机网络信息中心,2013.6.
《基于CUDA架构的并行数据挖掘软件》,2010年中日女科学家研讨会,2010.9.
《GUCAS_CUMiner: A CUDA-based Parallel Data Mining Toolkit》, 2010 International Workshop on GPU Solutions to Multiscale Problems in Science and Engineering, 2010.7.
《Utility Mining》, 国立新加坡大学,2010.1.
《源代码缺陷挖掘》, 中国科学院软件所/美国马萨诸塞大学联合研讨会,2009.6.
《基于图形处理器的并行计算》,国家气象局,2009.5.
《基于图形处理器的并行数据挖掘》,全国高性能计算新浪潮研讨会,2009.5.
《高性能数据挖掘及应用》,全国虚拟天文台研讨会,2008.11.
《高性能数据挖掘在科学计算中的应用》,全国科学数据库与信息技术研讨会,2008.10.
《数据挖掘在审计数据中的应用》,国家审计署,2007.9