



Author: suuri4
Lab 4. Hiromichi Nagao
Profile
Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Associate Professor
111 Yayoi, Bunkyoku, Tokyo, 1130032 Eat. 3 Bldg. Room 33
Tel: +81358411766 (ext. 21766)
Fax:+81358411766
Email： nagao@mist.t.utokyo.ac.jp
Curriculum Vitae
Mar. 1995  Bachelor degree from Faculty of Science, Kyoto University 

Mar. 1997  Master degree from Graduate School of Science, Kyoto University 
Mar. 2002  Ph. D., Graduate School of Science, Kyoto University 
Apr. 2002  Visiting Researcher, Japan Nuclear Cycle Development Institute 
Mar. 2006  Researcher, Japan Agency for MarineEarth Science and Technology 
Jun. 2009  Project Researcher, The Institute of Statistical Mathematics 
Dec. 2010  Project Associate Professor, The Institute of Statistical Mathematics 
Sep. 2013  Associate Professor, Earthquake Research Institute, The University of Tokyo 
Oct. 2013  Associate Professor, Graduate School of Information Science and Technology, The University of Tokyo 
Research Themes
We could not prevent the damage from spreading caused by the Great East Japan Earthquake, which took place on March 11, 2011, despite the recent developments of globalscale realtime observational networks and largescale numerical simulations based on highperformance computing.In order to save as many human lives as possible from future great earthquakes, we are dedicating to accumulate comprehensive knowledge through integration of observation and simulation data related to earthquakes, tsunamis and seismic hazards based on statistical methodologies such as data assimilation.
1. Data Assimilation
Data assimilation is a computational technique to integrate numerical simulation models and observational/experimental data based on Bayesian statistics. Data assimilation provides simulation models that are possible to predict the future, sequentially estimating parameters involved in the simulation models and state vectors at every time step. Data assimilation was originally developed in meteorology and oceanology; for example, the weather forecasting absolutely shows results of data assimilation. We develop data assimilation techniques for the solid Earth science to investigate earthquakes and tsunamis.
2. Sequential Bayesian Filters and FourDimensional Variational Method
In data assimilation, an appropriate method is to be selected from various types of sequential Bayesian filters or fourdimensional variational method (4DVar) to compare predictions obtained by numerical simulations and observational data, considering the purpose and computational cost. We have been developing new algorithms of sequential Bayesian filters and 4DVar that are suitable for practical problems in the solid Earth science.
Selected papers
 Sasaki, K., A. Yamanaka, S. Ito, and H. Nagao, Data assimilation for phasefield models based on the ensemble Kalman filter, Computational Materials Science, Vol. 141, pp. 141152, doi:10.1016/j.commatsci.2017.09.025, 2018.
 Ito, S., H. Nagao, T. Kasuya, and J. Inoue, Grain growth prediction based on data assimilation by implementing 4DVar on multiphasefield model, Science and Technology of Advanced Materials, Vol. 18, Issue 1, pp. 857869, doi:10.1080/14686996.2017.1378921, 2017.
 Kano, M., H. Nagao, K. Nagata, S. Ito, S. Sakai, S. Nakagawa, M. Hori, and N. Hirata, Seismic wavefield imaging of longperiod ground motion in the Tokyo Metropolitan area, Japan, J. Geophys. Res. Solid Earth, Vol. 122, doi:10.1002/2017JB014276, 2017.
 Kano, M., H. Nagao, D. Ishikawa, S. Ito, S. Sakai, S. Nakagawa, M. Hori, and N. Hirata, Seismic wavefield imaging based on the replica exchange Monte Carlo method, Geophys. J. Int., Vol. 208, pp. 529545, doi:10.1093/gji/ggw410, 2017.
 Ito, S., H. Nagao, A. Yamanaka, Y. Tsukada, T. Koyama, M. Kano, and J. Inoue, Data assimilation for massive autonomous systems based on a secondorder adjoint method, Phys. Rev. E, 94, 043307, doi:10.1103/PhysRevE.94.043307, 2016.
Lab. 4 Fumiyasu Komaki
Profile
Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Professor
731 Hongo, Bunkyoku, Tokyo, 1138656 Eng. 6 Bldg. Room 349
Tel: +81358416941 (ext. 26941)
Fax:+81358418592
Email： komaki@mist.i.utokyo.ac.jp
Curriculum Vitae
Mar. 1987  Bachelor degree from Department of Mathematical Engineering and Instrumentation Physics, Faculty of Engineering, The University of Tokyo 

Mar. 1989  Master degree from Department of Mathematical Engineering and Information Physics, Graduate School of Engineering, The University of Tokyo 
Mar. 1992  Ph. D. from Department of Statistical Science, School of Mathematical and Physical Science, The Graduate University for Advanced Studies 
Apr. 1992  Research Associate, Department of Mathematical Engineering and Information Physics, Faculty of Engineering, The University of Tokyo 
Apr. 1995  Associate Professor, The Institute of Statistical Mathematics, Ministry of Education, Science and Culture 
Oct. 1998  Associate Professor, Department of Mathematical Engineering and Information Physics, Graduate School of Engineering, The University of Tokyo 
Apr. 2001  Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo 
Aug. 2009  Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo 
Research Themes
1. Theoretical Statistics
Bayes theory, Prediction theory, Information geometry
2. Statistical Modeling
Statistical models and data analysis in neuroscience and seismology.
Selected papers
 Shibue, R. and Komaki, F. (2017). Firing rate estimation using infinite mixture models and its application to neural decoding, Journal of Neurophysiology, vol. 118, 2902–29.
 Yano, K. and Komaki, F. (2017). Asymptotically minimax prediction in infinite sequence models, Electronic Journal of Statistics, vol. 11, 31653195.
 Kojima, M. and Komaki, F. (2016). Relations between the conditional normalized maximum likelihood distributions and the latent information priors, IEEE Transactions on Information Theory, vol. 62, pp. 539553.
 Matsuda, T. and Komaki, F. (2015). Singular value shrinkage priors for Bayesian prediction, Biometrika, vol. 102, pp. 843854.
Tomonari Sei
Profile
Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Professor
731 Hongo, Bunkyoku, Tokyo, 1138656 Eng. 6 Bldg. Room 353
Tel:
Fax:
Email： sei@mist.t.utokyo.ac.jp
Curriculum Vitae
Mar. 2000  Bachelor degree from Department of Mathematical Engineering and Information Physics, Faculty of Engineering, The University of Tokyo 

Mar. 2002  Master degree from Department of Mathematical Engineering and Information Physics, Graduate School of Engineering, The University of Tokyo 
Mar. 2005  Ph. D. from Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo 
Apr. 2005  Assistant Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo 
Apr. 2011  Lecturer, Department of Mathematics, Faculty of Science and Technology, Keio University 
Apr. 2014  Associate Professor, Department of Mathematics, Faculty of Science and Technology, Keio University 
Apr. 2015  Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo 
Jun. 2021  Professor, Mathematics and Informatics Center, The University of Tokyo 
Research Themes
I study theoretical aspects of statistics (mathematical statistics).Selected papers
 Sei, T. and Kume, A. (2015). Calculating the normalizing constant of the Bingham distribution on the sphere using the holonomic gradient method, Statistics and Computing, 25 (2), 321332.
 Sei, T. (2014). Infinitely imbalanced binomial regression and deformed exponential families, Journal of Statistical Planning and Inference, 149, 116124.
 Rueschendorf, L. and Sei, T. (2012). On optimal stationary couplings between stationary processes, Electronic Journal of Probability, 17 (17), 120.
 Nakayama H., Nishiyama K., Noro M., Ohara K., Sei, T., Takayama, N. and Takemura A. (2011). Holonomic gradient descent and its application to the FisherBingham integral, Advances in Applied Mathematics, 47 (3), 639658.
Lab. 4 Yoshito Hirata
Profile
Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Associate Professor
731 Hongo, Bunkyoku, Tokyo, 1138656
Eng. 6 Bldg. Room 436
Tel: +81358410698
Fax:
Email： hirata@mist.i.utokyo.ac.jp
Curriculum Vitae
Mar. 1998  Bachelor degree from Department of Mathematical Engineering and Information Physics, The University of Tokyo 

Mar. 2000  Master degree from Department of Mathematical Engineering and Information Physics, The University of Tokyo 
Apr. 2004  Postdoctoral researcher, Department of Mathematical Informatics, The University of Tokyo 
Jun. 2004  Ph. D. from School of Mathematics and Statistics, University of Western Australia 
Oct. 2006  Researcher, Aihara Complexity Medelling Project, Japan Science and Technology Agency 
Apr. 2008  Project Research Associate, Institute of Industrial Science, The University of Tokyo 
Jul. 2010  Project Associate Professor, Institute of Industrial Science, The University of Tokyo 
Jan. 2018  Associate Professor, Mathematics and Informatics Center, The University of Tokyo 
Research Themes
I am investigating theory and applications of nonlinear time series analysis, which is time series analysis
based on dynamical systems theory.
Especially, I want to grasp new needs for analyzing real datasets, develop new methods for nonlinear
time series analysis for such purposes, and apply the methods to solve the real world problems by
understanding, predicting and/or controlling the underlying dynamics.
Selected papers
 Y. Hirata, T. Stemler, D. Eroglu, and N. Marwan, Prediction of flow dynamics using point processes, Chaos 28, 011101 (2018).
 Y. Hirata, A. Oda, K. Ohta, and K. Aihara, Threedimensional reconstruction of singlecell chromosome structure using recurrence plots, Scientific Reports 6, 34982 (2016).
 Y. Hirata, N. Bruchovsky, and K. Aihara, Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer, Journal of Theoretical Biology 264, 517527 (2010).
 Y. Hirata, K. Judd, and D. Kilminster, Estimating a generating partition from observed time series: Symbolic shadowing, Physical Review E 70, 016215 (2004).