宋轩Google Scholar ResearcherID

副教授 计算机科学与工程系
宋轩教授于2010年在北京大学获得博士学位，在2017年入选了日本国家卓越研究员计划。该项计划是日本国家最高级别的青年人才战略培育计划，当年仅72人入选，他是唯一的中国籍入选者。在过去六年中，宋轩教授获得了来自日本国家科学技术振兴机构（JST），日本学术振兴会（JSPS）以及日本国土交通省和微软研究院的资助，作为项目负责人（PI）主持并领导了8项科研项目，参与了多项日本政府和企业合作项目，包括日本国家战略项目（DIAS/GRENE Project），日立公司合作项目，日本国家铁路公司（JR）合作项目，NTT Docomo合作项目，Yahoo Japan合作项目等，累积获得经费资助超过10亿日元（主持和参与）。此外，宋轩教授在多个国际著名学术期刊和会议上任职编委，包括在CCF A类期刊IMWUT（原UbiComp会议）任职副编辑（Associate Editor），在IEEE Transactions on Multimedia，WWW Journal任职客座编辑，Big Data Journal任职副编辑（2014-2015），国际智能交通大会（ITSC）任职副编辑（Associate Editor），及IEEE 多媒体及信息检索大会（MIPR）任职领域主席（Area Chair）。
宋轩教授的主要研究方向为人工智能及其相关领域，包括大数据分析、数据挖掘和普适计算等。在过去10年间，他在计算机领域知名的国际期刊和会议上发表各类论文80余篇，其中发表在JCR一区或中国计算机协会推荐的A类期刊会议论文超过40篇。他发表在KDD 2014和ACM TIST 2017的论文首次提出一个精准的人类应急行为的预测模型，该项研究成果被联合国“全球脉动”（United Nations Global Pulse）和探索频道（Discovery Channel）以及美国最具影响力的科技杂志之一Fast Company重点报道。他发表在UbiComp 2015的论文首次提出了一个城市尺度的人流瞬时预测模型，该论文获得当年UbiComp年会（中国计算机学会推荐的A类会议）的最佳论文提名奖。美国国防部发布的2018年度关于城市人流预测和城市应急管理项目征集指南中，重点引用了他的多篇文章。
个人简介
个人简介

研究领域人工智能；大数据分析；城市计算；智慧城市

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学术成果 查看更多Journal paper:
(55) Q. Zhang, X. Song, Y. Yang, H. Ma, R. Shibasaki, “Visual graph mining for graph matching”, Computer Vision and Image Understanding, 2018.
(54) T. Li, Xuan Song, S.-C. Chen, R. Shibasaki, R. Akerkar “Editorial Introduction to the Special Issue on Multimedia Big Data for Extreme Events”, IEEE Trans. Multimedia 20(10): 2547-2550, 2018.
(53) Z. Fan, X. Song, T. Xia, R. Jiang, R. Shibasaki and R. Sakuramachi, “Online Deep Ensemble Learning for Predicting Citywide Human Mobility”, Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (UbiComp 2018), 2018.
(52) H. Zhang, X. Song, T. Xia, M. Yuan, Z. Fan, R. Shibasaki, Y. Liang, “Battery electric vehicles in Japan: Human mobile behavior based adoption potential analysis and policy target response”, Applied Energy, 2018.
(51) R. Jiang, X. Song, Z. Fan, T. Xia, Q. Chen, Q. Chen, and R. Shibasaki, “Deep ROI-Based Modeling for Urban Human Mobility Prediction”, Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (UbiComp 2018), 2018.
(50) S. Miyazawa, X. Song, T. Xia, R. Shibasaki, H. Kaneda, “Integrating GPS trajectory and topics from Twitter stream for human mobility estimation“, Frontiers of Computer Science, 2018.
(49) X. Song, R. Shibasaki, N. Yuan, X. Xie, T. Li, R. Adachi, “DeepMob: Learning Deep Knowledge of Human Emergency Behavior and Mobility from Big and Heterogeneous Data”, ACM Transactions on Information Systems (ACM TOIS), 35(4): 41, 19 pages, 2017.
(48) X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, N. Yuan, X. Xie, “Prediction and Simulation of Human Mobility Following Natural Disasters”, ACM Transactions on Intelligent Systems and Technology (ACM-TIST), 8(2): 29, 2017.
(47) Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki,”Object Discovery: Soft Attributed Graph Mining”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 38(3): 532-545, 2016.
(46) J Dou, D.T Bui, A.P. Yunus, K. Jia, X. Song, I. Revhaug, H. Xia, Z. Zhu, “Optimization of Causative Factors for Landslide Susceptibility Evaluation using Remote Sensing and GIS data in parts of Niigata”, PLOS ONE, 2015.
(45) Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, “From RGB-D Images to RGB Images: Single Labeling for Structural Model Mining”, ACM Transactions on Intelligent Systems and Technology (ACM-TIST), 6(2): 16, 2015.
(44) J Dou, H Yamagishi, HR Pourghasemi, AP Yunus, X Song, Y Xu, Z Zhu, “An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan”, Natural Hazards, pp. 1-28, 2015.
(43) X. Song, “Disaster Behavior Analysis and Urban Emergency Management in the Era of Big Data”, Communications of the CCF, vol. 9, no. 8, pp. 25-29, 2013.
(42) X. Song, Q. Zhang, Y. Sekimoto, T. Horanont, S. Ueyama, R. Shibasaki, “Intelligent System for Human Behavior Analysis and Reasoning Following Large-Scale Disasters,” IEEE Intelligent Systems, vol. 28, no. 4, pp. 35-42, July-Aug. 2013.
(41) X. Song, J. Cui, H. Zhao, H. Zha, R. Shibasaki, ” Laser-based Tracking of Multiple Interacting Pedestrians via On-line Learning”, Neurocomputing, Elsevier, pp. 170-182, 2013.
(40) X. Song, X. Shao, Q. Zhang, R. Shibasaki, H. Zhao, J. Cui, H. Zha, “A Fully Online and Unsupervised System for Large and High Density Area Surveillance: Tracking, Semantic Scene Learning and Abnormality Detection”, ACM Transactions on Intelligent Systems and Technology (ACM-TIST), 4(2): 35, 2013.
(39) Q. Zhang, X. Song, X. Shao, R. Shibasaki, H. Zhao, “Unsupervised skeleton extraction and motion capture from 3D deformable matching”, Neurocomputing, Elsevier, pp.170-182, 2013.
(38) X. Song, H. Zhao, J. Cui, X. Shao, R. Shibasaki, H. Zha, “An Online System for Multiple Interacting Targets Tracking: Fusion of Laser and Vision, Tracking and Learning”, ACM Transactions on Intelligent Systems and Technology (ACM-TIST), 4(1): 18, 2013.
(37) X. Song, X. Shao, Q. Zhang, R. Shibasaki, H. Zhao, H. Zha, “A Novel Dynamic Model for Multiple Pedestrians Tracking in Extremely Crowded Scenarios”, Information Fusion, Elsevier, 14(3), pp. 301-310, 2013.
(36) X. Song, J. Cui, H. Zhao, H. Zha, “A Bayesian Approach: Fusion of Laser and Vision for Multiple Pedestrians Tracking”, International Journal of Advanced Computer Engineering (IJACE), pp. 52-63, 2009.
Conference paper:
(35) Z. Fan, X. Song, T. Xia, R. Jiang, R. Shibasaki and R. Sakuramachi, “Online Deep Ensemble Learning for Predicting Citywide Human Mobility”, Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2018.
(34) R. Jiang, X. Song, Z. Fan, T. Xia, Q. Chen, Q. Chen, and R. Shibasaki, “Deep ROI-Based Modeling for Urban Human Mobility Prediction”, Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2018.
(33) R. Jiang, X. Song, Z. Fan, T. Xia, Q. Chen, S. Miyazawa, R. Shibasaki, “DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction”, Proc. of Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018.
(32) T. Xia, X. Song, Z. Fan, H. Kanasugi, Q. Chen, R. Jiang, R. Shibasaki, “DeepRailway: A Deep Learning System for Forecasting Railway Traffic”, Proc. of IEEE 2018 International Conference on Multimedia Information Processing and Retrieval (MIPR), 2018.
(31) Q. Chen, X. Song, Z, Fan, T, Xia, H. Yamada, R. Shibasaki “A Context-Aware Nonnegative Matrix Factorization Framework for Traffic Accident Risk Estimation via Heterogeneous Data”, Proc. of IEEE 2018 International Conference on Multimedia Information Processing and Retrieval (MIPR), 2018.
(30) T. Xia, X. Song, D. Huang, S. Miyazawa, Z. Fan, R. Jiang, R. Shibasaki, “Outbound Behavior Analysis Through Social Network Data: a case study of Chinese people in Japan”, Proc. of Big Social Media Data Management and Analysis, IEEE Big Data, 2017.
(29) Z. Fan, X. Song, R. Shibasaki, T. Li, R. Adachi, “CityCoupling: Bridging Intercity Human Mobility”, Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2016.
(28) X. Song, H. Kanasugi, R. Shibasaki, “DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level”, Proc. of 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016.
(27) Z. Fan, A. Arai, X. Song, A. Witayangkurn, H. Kanasugi, R. Shibasaki, ”A Collaborative Filtering Approach to Citywide Human Mobility Completion from Sparse Call Records”, Proc. of 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016.
(26) Q. Chen, X. Song, H. Yamada, R. Shibasaki, “Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference”, Proc. of Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), pp. 338-344, 2016.
(25) A. Sudo, T. Kashiyama, T. Yabe, H. Kanasugi, X. Song, T. Higuchi, S. Nakano, M. Saito and Y. Sekimoto, “Particle Filter for Real-time Human Mobility Prediction following Unprecedented Disaster”, Proc. of 24th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), 2016.
(24) X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, N. Yuan, X. Xie, “A Simulator of Human Emergency Mobility following Disasters: Knowledge Transfer from Big Disaster Data”, Proc. of Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), pp. 730-736, 2015.
(23) Z. Fan, X. Song, R. Shibasaki, R. Adachi, “CityMomentum: An Online Approach for Crowd Behavior Prediction at a Citywide Level”, Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2015.
(22) X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, “Prediction of Human Emergency Behavior and their Mobility following Large-scale Disaster”, Proc. of 20th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2014), pp. 5-14, 2014.
(21) Z. Fan, X. Song, R. Shibasaki, “CitySpectrum: A Non-negative Tensor Factorization Approach”, Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 213-223, 2014.
(20) X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, “Intelligent System for Urban Emergency Management During Large‐scale Disaster”, Proc. of Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), pp. 458-464, 2014.
(19) Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, “When 3D Reconstruction Meets Ubiquitous RGB-D Images”, Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 700-707, 2014.
(18) Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, “Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns”, Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp.1394-1401, 2014.
(17) Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, “Start from Minimum Labeling: Learning of 3D Object Models and Point Labeling from a Large and Complex Environment”, Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 3082-3089, 2014.
(16) Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, “Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes”, Proc. of IEEE International Conference on Computer Vision (ICCV), pp. 1329-1336, 2013.
(15) X. Song, Q. Zhang, Y. Sekimoto, T. Horanont, S. Ueyama, R. Shibasaki, “Modeling and Probabilistic Reasoning of Population Evacuation During Large-scale Disaster”, Proc. of 19th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2013), pp. 1231-1239, 2013.
(14) Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, , “Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models”, Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 193-200, 2013.
(13) Q. Zhang, X. Song, X. Shao, H. Zhao, R. Shibasaki, “Unsupervised 3D Category Discovery and Point Labeling from a Large Urban Environment”, Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 2685-2692, 2013.
(12) X. Song, X. Shao, Q. Zhang, R. Shibasaki, H. Zhao, H. Zha, “Laser-based Intelligent Surveillance and Abnormality Detection in Extremely Crowded Scenarios”, Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 2170-2176, 2012.
(11) X. Song, X. Shao, R. Shibasaki, H. Zhao, J. Cui, H. Zha, “A novel laser-based system: Fully online detection of abnormal activity via an unsupervised method”, Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp.1317-1322, 2011.
(10) X. Song, X. Shao, H. Zhao, J. Cui, R. Shibasaki, H. Zha, “An Online Approach: Learning-Semantic-Scene-by-Tracking and Tracking-by-Learning-Semantic-Scene”, Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp.1652-1659, 2010.
(9) X. Song, H. Zhao, J. Cui, X. Shao, R. Shibasaki, H. Zha, “Fusion of Laser and Vision for Multi-target Tracking via On-line Learning”, Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp.406-411, 2010.
(8) X. Song, B. Wen, J.Cui, H. Zhao, X. Shao, R. Shibasaki, H. Zha, “A Boosted JPDA Particle Filter for Multi-target Tracking”, Proc. of in Asian Workshop on Sensing and Visualization of City-Human Interaction (AWSVCI ), pp.5-8,2009.
(7) H. Zha, H. Zhao, J. Cui, X. Song, X. Ying, “Combining Laser-Scanning Data and Images for Target Tracking and Scene Modeling”, Proc. of the 14th International Symposium on Robotics Research (ISRR), 2009.
(6) X. Song, J. Cui, H. Zha, H. Zhao, “Vision-based Multiple Interacting Targets Tracking via On-line Supervised Learning”, Proc. of European Conference on Computer Vision (ECCV), pp.642-655, 2008.
(5) X. Song, J. Cui, H. Zha, H. Zhao, “Probabilistic Detection-based Particle Filter for Multi-target Tracking”, Proc. of British Machine Vision Conference (BMVC), pp.223-232, 2008.
(4) X. Song, J. Cui, H. Zhao, H. Zha, “Bayesian Fusion of Laser and Vision for Multiple People Detection and Tracking”, Proc. of IEEE International Conference on Instrumentation, Control and Information Technology (SICE), pp.3014-3019, 2008.
(3) X. Song, J. Cui, X. Wang, H. Zhao, H. Zha, “Tracking Interacting Targets with Laser Scanner via On-line Supervised Learning”, Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp.2271-2276, 2008.
Book Chapter:
(2) H. Zha, H. Zhao, J. Cui, X. Song and X. Ying, “Combining Laser Scanning Data and Images for Target Tracking and Scene Modeling”, Robotics Research, Springer Tracts in Advanced Robotics, Volume 70/2011, Springer, ISBN 978-3-642-19456-6, pp. 573-587, 2011.
(1) J. Cui, X. Song, H. Zhao, H. Zha, R. Shibasaki, “Real-time Detection and Tracking of Multiple People in Laser Scan Frames”, Springer Book: Augmented Vision Perception in Infrared, 2009.



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