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Author: suuri4
Lab 4. Hiromichi Nagao
Profile
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
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
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.i.u-tokyo.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, 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.
Tomonari Sei
Profile
Tomonari Sei
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 353
Tel:
Fax:
E-mail： sei@mist.t.u-tokyo.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).
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
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
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).