Faculty Staff Information

Gouhei TANAKA

Project Associate Professor
International Research Center for Neurointelligence
Department of Mathematical Informatics, Graduate School of Information Science and Technology
Department of Electrical Engineering and Information Systems, Graduate School of Engineering

Room N308, IRCN, Faculty of Medicine Bldg. 1, The University of Tokyo, 3-7-1 Bunkyo-ku, Hongo, Tokyo 113-0033, Japan.
Phone: not available

E-mail：gtanaka＠g.ecc.u-tokyo.ac.jp

[Website]

Research theme

1. Brain-like energy-efficient information processing

   For realizing next-generation information processing systems, it is indispensable to miniaturize devices and make structures compact for enhancement of energy efficiency. We aim to develop mathematical methodologies for making brain-like computing systems energy efficient such that efficient computing is realized with low power and high speed.

2. Applications of machine learning and advanced mathematical methods

   Machine learning technologies have enabled to efficiently perform tasks that have been manually handled by people. We aim to mathematically formulate problems in fields that are not approached by machine learning and mathematical modeling, and solve the problems by combining appropriate machine learning methods and advanced mathematical techniques.

3. Mathematical studies on medical and social systems

   It is becoming possible to obtain real data on medical and social systems due to the developments of sensor devices and measurement techniques. We aim to propose effective control strategies for solving medically and socially important problems and improving quality of life.                                               

4. Network robustness

   Networked systems are ubiquitous in the world, such as the Internet, power networks, and biological networks. Networking often accompanies a risk that a partial failure causes a breakdown of the whole system. We are investigating how network robustness depends on network structure, dynamics, and element interactions. Our aim is to develop a design method of robust networks and a recovery method of damaged networks.

Recent Publications

G. Tanaka, R. Nakane, T. Takeuchi, T. Yamane, D. Nakano, Y. Katayama, and A. Hirose
Spatially Arranged Sparse Recurrent Neural Networks for Energy Efficient Associative Memory
IEEE Transactions on Neural Networks and Learning Systems, vol. 31, issue 1, pp. 24-38 (2020). DOI: 10.1109/TNNLS.2019.2899344

A. Matsuki and G. Tanaka
Intervention threshold for epidemic control in susceptible-infected-recovered metapopulation models
Physical Review E, vol. 100, 022302 (2019).

G. Tanaka et al.
Recent Advances in Physical Reservoir Computing: A Review
Neural Networks, vol. 115, pp. 100-123 (2019).

Z. Tong and G. Tanaka
Hybrid pooling for enhancement of generalization ability in deep convolutional neural networks
Neurocomputing, vol. 333. pp. 76-85 (2019).

G. Tanaka, E. Dominguez-Huttinger, P. Christodoulides, K. Aihara, and R. J Tanaka
Bifurcation analysis of a mathematical model of atopic dermatitis to determine patient-specific effects of treatments on dynamic phenotypes
Journal of Theoretical Biology, vol. 448, pp. 66-79 (2018).

Person

Ayumu Matani

Associate Professor
Department of Information Physics and Computing
Graduate School of Information Science and Technology

7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656
Tel: 03-5841-7768
Fax:

E-mail：matani＠isp.ac

[web page]

Career

Research Projects

We study cognitive neuroengineering, the engineering background is signal processing, instrumentation, information and communication, and electric and electronic circuit.

In electroencephalogram (EEG) recording associated with cognitive science experiments, the independent variables of the EEG are time, space (EEG channel), and epoch (or trial).  In analyses of the EEG, a variety of temporal and spatial filters have been proposed so far and they play intrinsic and exclusive roles with respect to each other.  If any epoch filter were created, it would provide a special role that both temporal and spatial filters are not able to play.  For instance, we proposed epoch filters in order to analyze cross frequency coupling (2,3).

In the electrophysiological mechanism of neurons, the post-synaptic potentials generate dendritic currents, the dendritic currents flow out from neurons as a distributed current in the head after producing membrane potentials, and return to the original neurons.  When the spatial sum of the dendritic current is measured as a voltage drop on the scalp, the measuemnt will be EEG.  If an impedance is attached on the scalp,  it modulates a portion of the dendritic currents and thereby would indirectly have an effect on the membrane potentials that originate the portion.  For instance, we successfully shortened reaction time of a visual selective response task (1).

Publications

1) A. Matani, M. Nakayama, M. Watanabe, Y. Furuyama, A. Hotta, and S. Hoshino, Transcranial extracellular impedance control (tEIC) modulates behavioral performances, PLoS ONE, e0102834, 2014.

2) A. Matani, Y. Naruse, Y. Terazono, N. Fujimaki, and T. Murata, Phase-Interpolated Averaging for Analyzing Electroencephalography and Magnetoencephalography Epochs, IEEE Trans. on Biomedical Engineering, vol. 58, no. 1, pp. 71-80, 2011.

3) A. Matani, Y. Naruse, Y. Terazono, T. Iwasaki, N. Fujimaki, and T. Murata, Phase-Compensated Averaging for Analyzing Electroencephalography and Magnetoencephalography Epochs, IEEE Trans. on Biomedical Engineering, vol. 57, no. 5, pp. 1117-1123, 2010.

Profile

Shin-ichi Tanigawa

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 340
Tel: 03-5841-6906, ext. 26906
Fax:

E-mail：tanigawa＠mist.i.u-tokyo.ac.jp

[Home Page]

 

Curriculum Vitae

Research Themes

● Discrete and Computational Geometry
Design and analysis of algorithms for geometric problems in engineering. Topics of particular interest are: rigidity theory and geometric graph theory.  

● Discrete Algorithms
Design and analysis of algorithms for discrete optimization problems. Topics of particular interest are: graph algorithms and combinatorial optimization.

Selected Publications

Satoru Fujishige and Shin-ichi Tanigawa: Polynomial combinatorial algorithms for skew-bisubmodular function minimization, Mathematical Programming, to appear, 2017.

Shin-ichi Tanigawa: Singularity degree of the positive semidefinite matrix completion problem, SIAM Journal on Optimization, 27, 986–1009, 2017.

Bill Jackson, Tibor Jordan and Shin-ichi Tanigawa: Unique low rank completability of partially filled matrices, Journal of Combinatorial Theory, Series B, 121, 432-462, 2016.

Shin-ichi Tanigawa: Sufficient conditions for globally rigidity of graphs, Journal of Combinatorial Theory Series B, 113: 123–140, 2015.

Shin-ichi Tanigawa: Matroids of gain graphs in applied discrete geometry. Transactions of the American Mathematical Society, 367, 8597-8641, 2015.

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

[Home Page]

Curriculum Vitae

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.

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

[Home Page]

Curriculum Vitae

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.

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

[Home Page]

Curriculum Vitae

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), 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.