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Shogo Okada Associate Professor
School of Information Science

■Degrees

B.S. from Yokohama National University (2003), M.S. from Tokyo Institute of Technology (2005), Ph.D from Tokyo Institute of Technology (2008)

■Professional Career

Project Assistant Professor at Kyoto University (2008), Assistant Professor at Tokyo Institute of Technology (2011)Visting fuculty at IDIAP research institute (2014)

■Specialties

Intelligent Information Processing, Social Intelligence,Social Computing,

■Research Keywords

Social Signal Processing, Multimodal Interaction, Human dynamics, Machine learning , Data mining

■Research Interests

Human dynamics and social signal processing based on multimodal machine learning and data mining, and it's application for communicative robot/ agen.
(1)Multimodal Interaction Modeling: Face to face conversation is a fundamental communication method for information sharing, decision making and consensus building. It has various kind of types: casual talking with friends, business meeting, negotiation, counseling, and so on. People send not only verbal information, but also nonverbal information each other. Their role in conversation, attitude (e.g. passive, active, cooperative), intention (e.g. agree), and emotional state sometimes can be observed from their multimodal (verbal and nonverbal) signals called social signals [A.Vinciarelli et al 2009]. Conversational states such as lively discussion and to be silent can be observed by fusing social signals of all members. My researches focus on building computational model of multimodal social signals (speech, gaze, gesture and so on.) by using speech signal processing, image processing, motion sensor processing and pattern recognition techniques. My research question is how these social signal patterns influence high level output and tacit knowledge such as output after consensus building, communication skills and explanation skills. These modeling techniques can be also used to develop a sensing module for conversational robots/agents.(2) Human Dynamics Modeling: Recent progress in developing sensors: location sensors, for monitoring human motion and activity has become available for analyzing longitudinal human activity and it’s dynamics in real environment. A research focuses on analyzing of office worker’s activity from sensor environment set in office. Another research focuses on analyzing of driver’s behaviors (brake patterns, how to press the accelerator or the brakes) from sensor environment equipped in cars.(3) Machine Learning and Data Mining: Phenomenon of multimodal interaction and human dynamics are observed as continuous multi-dimensional time-series data from multiple sensors. Machine learning techniques are important to build recognition model from these multidimensional time-series data set. It is difficult to define Social signal patterns and typical activity patterns in office and represent features of it. To discover the structure of these patterns, Data mining algorithm are also useful. In particular, we focus on developing time-series clustering, multidimensional motif discovery and change point detection algorithm and applied to find various patterns and structure of data

■Publications

◇Published Papers

  • Predicting performance of collaborative storytelling using multimodal analysis,Shogo Okada, Mi Hang, Katsumi Nitta,IEICE Transactions,E99-D,6,1462-1473,2016
  • Incremental Learning of Gestures for Human-Robot Interaction,Shogo Okada, Yoichi Kobayashi, Satoshi Ishibashi, Toyoaki Nishida,, Springer Journal of AI & Society, Vol. 25, Num. 2 , pp.155-168 (2010),2010
  • 自己増殖型ニューラルネットワークを用いた時系列データの追加学習型クラスタリング,岡田将吾,西田豊明,,日本神経回路学会論文誌, Vol.17,No.4, 174-186, (2010),2010

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■Extramural Activities

◇Academic Society Affiliations

  • ACM, IEICE, JSAI,2013-

◇Other Activities

  • JSAI,Editor
  • IPSJ,Editor
  • JSAI SIG-SLUD,2012/04/01 - 2020/03/31

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■Academic Awards Received

  • JSAI Incentive Award,The Japanese Society for Artificial Intelligence,2016