



Month: February 2018
Professor Gouhei TANAKA
Faculty Staff Information
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, 371 Bunkyoku, Hongo, Tokyo 1130033, Japan.
Phone: not available
Email：gtanaka＠g.ecc.utokyo.ac.jp
[Website]
Research theme

Brainlike energyefficient information processing
For realizing nextgeneration 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 brainlike computing systems energy efficient such that efficient computing is realized with low power and high speed. 
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. 
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. 
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. 2438 (2020). DOI: 10.1109/TNNLS.2019.2899344A. Matsuki and G. Tanaka
Intervention threshold for epidemic control in susceptibleinfectedrecovered 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. 100123 (2019).Z. Tong and G. Tanaka
Hybrid pooling for enhancement of generalization ability in deep convolutional neural networks
Neurocomputing, vol. 333. pp. 7685 (2019).G. Tanaka, E. DominguezHuttinger, P. Christodoulides, K. Aihara, and R. J Tanaka
Bifurcation analysis of a mathematical model of atopic dermatitis to determine patientspecific effects of treatments on dynamic phenotypes
Journal of Theoretical Biology, vol. 448, pp. 6679 (2018). 
System #1 lab.




Ayumu Matani
Person
Associate Professor
Department of Information Physics and Computing
Graduate School of Information Science and Technology
731 Hongo, Bunkyoku, Tokyo 1138656
Tel: 0358417768
Fax:
Email：matani＠isp.ac
[web page]
Career
1991  M.Eng, Graduate School of Scientific Engineering, Osaka University 

1991  Researcher, Osaka Gas Co. Ltd. 
1995  Assistant, Graduate School of Information Technology, Nara Institute of Science and Technology 
1998  Ph. D, Graduate School of Scientific Engineering, Osaka University 
1998  Assistant Professor, Graduate School of Engineering, the University of Tokyo 
1999  Associate Professor, Graduate School of Frontier Sciences, the University of Tokyo 
2012  Associate Professor, Graduate School of Information Science and Technology, the University of Tokyo 
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 postsynaptic 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, PhaseInterpolated Averaging for Analyzing Electroencephalography and Magnetoencephalography Epochs, IEEE Trans. on Biomedical Engineering, vol. 58, no. 1, pp. 7180, 2011.
 3) A. Matani, Y. Naruse, Y. Terazono, T. Iwasaki, N. Fujimaki, and T. Murata, PhaseCompensated Averaging for Analyzing Electroencephalography and Magnetoencephalography Epochs, IEEE Trans. on Biomedical Engineering, vol. 57, no. 5, pp. 11171123, 2010.
Suri7Tanigawa
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 340
Tel: 0358416906, ext. 26906
Fax:
Email：tanigawa＠mist.i.utokyo.ac.jp
Curriculum Vitae
Mar. 2005  Graduated from the Department of Architecture and Architectural Engineering, Faculty of Engineering, Kyoto University 

Mar. 2007  Graduated from the Master Course of the Department of Architecture and Architectural Engineering, Graduate School of Engineering, Kyoto University 
Mar. 2010  Graduated from the the Doctor Course of the Department of Architecture and Architectural Engineering, Graduate School of Engineering, Kyoto University 
Apr. 2010 – May 2011  Postdoctoral Fellow of Japan Society for the Promotion of Science 
Jun. 2011 – Mar. 2017  Assistant Professor, Research Institute for Mathematical Sciences, Kyoto University 
Apr. 2017 –  Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo 
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 Shinichi Tanigawa: Polynomial combinatorial algorithms for skewbisubmodular function minimization, Mathematical Programming, to appear, 2017.
 Shinichi Tanigawa: Singularity degree of the positive semidefinite matrix completion problem, SIAM Journal on Optimization, 27, 986–1009, 2017.
 Bill Jackson, Tibor Jordan and Shinichi Tanigawa: Unique low rank completability of partially filled matrices, Journal of Combinatorial Theory, Series B, 121, 432462, 2016.
 Shinichi Tanigawa: Sufficient conditions for globally rigidity of graphs, Journal of Combinatorial Theory Series B, 113: 123–140, 2015.
 Shinichi Tanigawa: Matroids of gain graphs in applied discrete geometry. Transactions of the American Mathematical Society, 367, 85978641, 2015.
System Information Second Laboratory




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.t.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.
Lab. 4 Tomonari Sei
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 344
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 
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.
Mathematical Informatics 1st Laboratory



