Xuan Song

song

Dr. Xuan Song, Project Associate Professor

IPUC Group, Shibasaki Laboratory, Center for Spatial Information Science, The University of Tokyo.

Email: songxuan/at/csis.u-tokyo.ac.jp
Fax:+81-04-7136-4292

Biography:

Xuan Song received the Ph.D. degree in signal and information processing from Peking University, China, in 2010. From 2010 to 2012, he worked in Center for Spatial Information Science, The University of Tokyo as a post-doctoral researcher. From 2012 to 2015, he worked in Center for Spatial Information Science, The University of Tokyo as a Project Assistant Professor. In 2015, he was promoted to Project Associate Professor with the Center for Spatial Information Science, The University of Tokyo. In the past five years, he led and participated in some important projects as principal investigator or primary actor in Japan, such as DIAS/GRENE Grant of MEXT, Japan; Japan/US Big Data and Disaster Project of JST, Japan; Young Scientists Grant of MEXT, Japan; Research Grant of MLIT, Japan; CORE Project of Microsoft; Grant of JR EAST Company and Hitachi Company, Japan. His main research interest are AI and its related research areas, such as data mining, intelligent system, computer vision, and robotics, especially on intelligent surveillance/reasoning system design, mobility and spatio-temporal data mining, sensor fusion, and machine learning algorithms development. By now, he has published more than 40 technical publications in journals, book chapter, and international conference proceedings, including more than 30 high-impact papers in top-tier publications for computer science and robotics, such as ACM TOIS, ACM TIST, IEEE TPAMI, IEEE Intelligent System, KDD, UbiComp, IJCAI, AAAI, ICCV, CVPR, ECCV, ICRA and etc. His research was featured in many Japanese and international media, including United Nations, the Discovery Channel, and Fast Company Magazine. He received Honorable Mention Award in UbiComp 2015. For more details about his research, please visit here.

My Research:

For the details about my research, please visit here.

Funded Projects (selected-Full List):

  • Principal Investigator, Grant-in-Aid for Scientific Research B (17H01784) of Japan’s Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan: DeepMob: Learning Deep Models from Big and Heterogeneous Data for Next-generation Urban Emergency Management, 2017‐2020. Link
  • Principal Investigator, US/Japan Big Data and Disaster Project, Japan Science and Technology Agency (JST), Japan: Data-Driven Critical Information Exchange in Disaster Affected Public-Private Networks,  2015-2017. Link
  • Principal Investigator, Grant-in-Aid for Young Scientists (26730113) of Japan’s Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan: Intelligent System for Urban Emergency Management,  2014-2016. Link
  • Principal Investigator, Research Grant of Microsoft Research, USA: Urban Informatics: Urban Emergency Management and Big-Data,  2014-2015.
  • Principal Investigator, Grant-in-Aid for Young Scientists (23700192) of Japan’s Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan: Abnormal Activity Detection in High Density and Crowded Public Area with Distributed Sensors,  2011-2013. Link
  • Principal Investigator, Research Grant of Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan: Evacuation Behaviour Analysis and Simulation during The Great East Japan Earthquake and Fukushima Nuclear Accident,  2012-2013. Link
  • Principal Investigator, CORE Project of Microsoft Research, USA: Kinect-based Intelligent Surveillance, Motion Capture and 3D Object Recognition, 2012-2013. Details

Professional Services:

I served as Associate Editor for:

Big Data Journal, 2014-2015

I served as reviewer or program committee member for:

ACM Trans. on Intelligent Systems and Technology, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Intelligent Transportation Systems, IEEE Trans. on Emerging Topics in Computing, IEEE Trans. on SMC (Part A and B), Signal Processing, International Journal of Social Robotics, IEEE Communications Letters, Neurocomputing, The Visual Computer, IET Intelligent Transport Systems, Remote Sensing.

ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) 2016, ACM International Conference on Web Search and Data Mining (WSDM) 2017, IEEE International Conference on Computer Vision (ICCV) 2011-2015, European Conference on Computer Vision (ECCV) 2012-2016, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2011-2017, IEEE International Conference on Robotics and Automation (ICRA) 2008-2017, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2008-2016.

Selected Publications (premier journals or top-tier confferences) – Full List

(Authors associated with * are/were doctoral or master student I have supervised)
  • 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”, accepted by ACM Transactions on on Information Systems (ACM TOIS), 2017.
  • 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. PDF
  • 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), pp. 718-728, 2016. PDF
  • 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), pp. 2618-2624,  2016. PDF
  • 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), pp. 2500-2506, 2016. PDF
  • 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. PDF
  • 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. PDF
  • 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), pp. 559-569, 2015 (Honorable Mention Award). PDF Slide
  • 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. PDF
  • 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. PDF
  • X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, “Prediction of Human Emergency Behavior and their Mobility following Large-scale Disaster”, in Proc. of 20th SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2014),  pp. 5-14, 2014. PDF
  • Z. Fan*, X. Song, R. Shibasaki, “CitySpectrum: A Non-negative Tensor Factorization Approach”, in Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 213-223, 2014. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF
  • 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): 20, 2013. PDF
  • 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. PDF
  • Q. Zhang*, X. Song, X. Shao, H. Zhao, R. Shibasaki, “Semi-supervised 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.
  • Q. Zhang*, X. Song, X. Shao, H. Zhao, R. Shibasaki, “Learning Graph Matching for Category Modeling from Cluttered Scenes”, Proc. of IEEE International Conference on Computer Vision (ICCV),  pp. 1329-1336, 2013. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF
  • 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. PDF