Mathematical Informatics 4th Laboratory

Statistical Informatics Laboratory(Mathematical Informatics 4th Laboratory)
– Deep Theory and Wide Applications. That’s Statistics –
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Fumiyasu Komaki
Fumiyasu Komaki

Professor
Tomonari Sei
Tomonari Sei

Associate Professor
Hiromichi Nagao
Hiromichi Nagao

Associate Professor
Teppei Ogihara
Teppei Ogihara

Associate Professor
Theoretical Statistics
We establish the basis of statistical methods. A wide range of mathematical tools such as information geometry, algebraic methods and algorithms play an essential role as well as probability theory.

Statistical Modeling
Statistical methods are used in various fields such as brain science, geoscience, finance, medical science, quantum information and sports science. We are developing specific statistical models for analyzing complex phenomena in the real world.

Data Assimilation
Data assimilation integrates large-scale numerical simulation models and observational big data based on Bayesian statistics. We develop algorithms of data assimilation towards applications to practical problems.

Understanding, Predicting and Controlling Dynamics
Dynamics are hidden in a lot of things in the real world such as weather, renewable energy, earthquakes, economics, brain, lives and diseases. We are studying time series analysis for understanding, predicting and/or controlling them.

Lab 4. Hiromichi Nagao

Profile

Hiromichi Nagao
Hiromichi Nagao

Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Associate Professor

1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-0032 Eat. 3 Bldg. Room 33
Tel: +81-3-5841-1766 (ext. 21766)
Fax:+81-3-5841-1766

E-mail: nagao@mist.t.u-tokyo.ac.jp

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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 Marine-Earth 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 global-scale real-time observational networks and large-scale numerical simulations based on high-performance 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 Four-Dimensional Variational Method
In data assimilation, an appropriate method is to be selected from various types of sequential Bayesian filters or four-dimensional 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 phase-field models based on the ensemble Kalman filter, Computational Materials Science, Vol. 141, pp. 141-152, 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 multi-phase-field model, Science and Technology of Advanced Materials, Vol. 18, Issue 1, pp. 857-869, 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 long-period 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. 529-545, 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 second-order adjoint method, Phys. Rev. E, 94, 043307, doi:10.1103/PhysRevE.94.043307, 2016.

 

Lab. 4 Fumiyasu Komaki

Profile

Fumiyasu Komaki
Fumiyasu Komaki

Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Professor

7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656 Eng. 6 Bldg. Room 349
Tel: +81-3-5841-6941 (ext. 26941)
Fax:+81-3-5841-8592

E-mail: komaki@mist.t.u-tokyo.ac.jp

[Home Page]

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, 3165-3195.
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. 539-553.
Matsuda, T. and Komaki, F. (2015). Singular value shrinkage priors for Bayesian prediction, Biometrika, vol. 102, pp. 843-854.

 

Lab. 4 Tomonari Sei

Profile

Tomonari Sei
Tomonari Sei

Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Associate Professor

7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656 Eng. 6 Bldg. Room 344
Tel:
Fax:

E-mail: sei@mist.t.u-tokyo.ac.jp

[Home Page]

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

Research Themes

I study theoretical aspects of statistics (mathematical statistics).
1. Computational algebraic statistics: application of the holonomic gradient method to statistics.
2. Statistical modeling of rare events and time series data.
3. Statistical methods using optimal transport maps.

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), 321-332.
Sei, T. (2014). Infinitely imbalanced binomial regression and deformed exponential families, Journal of Statistical Planning and Inference, 149, 116-124.
Rueschendorf, L. and Sei, T. (2012). On optimal stationary couplings between stationary processes, Electronic Journal of Probability, 17 (17), 1-20.
Nakayama H., Nishiyama K., Noro M., Ohara K., Sei, T., Takayama, N. and Takemura A. (2011). Holonomic gradient descent and its application to the Fisher-Bingham integral, Advances in Applied Mathematics, 47 (3), 639-658.

 

Lab. 4 Yoshito Hirata

Profile

Yoshito Hirata
Yoshito Hirata

Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo
Associate Professor

7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656
Eng. 6 Bldg. Room 436
Tel: +81-3-5841-0698
Fax:

E-mail: hirata@mist.i.u-tokyo.ac.jp

[Home Page]

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, Three-dimensional reconstruction of single-cell 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, 517-527 (2010).
Y. Hirata, K. Judd, and D. Kilminster, Estimating a generating partition from observed time series: Symbolic shadowing, Physical Review E 70, 016215 (2004).