Collaborative Research Center for Innovative Mathematical Modeling

 

Collaborative Research Center for Innovative Mathematical Modeling (Institute of Industrial Science at the University of Tokyo)
– Challenge for Complex Dynamics in the Real World –

Website→
合原 一幸
Kazuyuki AIHARA

Professor
田中 剛平
Gouhei TANAKA

Project Associate Professor
近江 崇宏
Takahiro OMI

Project Associate Professor
Fundamental and Application Studies on Mathematical Modelling of Complex Systems
We perform theoretical studies for mathematical modelling and analysis of complex systems and application studies for real-world phenomena including artificial intelligence and power grids.
Complex Dynamics Analysis
Complex dynamical behavior is ubiquitous in a variety of phenomena, ranging from microscopic activities in cells and genes to macroscopic activities in earth and cosmos.  Towards understanding complex dynamical behavior and solving practical issues through mathematical modeling and analyses, we aim at developing a method for predicting, controlling, and optimizing complex dynamical phenomena. The subjects of this research include biology, medicine, public health, engineering, economics, and social problems. 

Professor Gouhei TANAKA

Faculty Staff Information

Gouhei TANAKA
田中 剛平

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).

    System #1 lab.

    System #1 lab
    – Signal processing –
    Lab HomePage→
    猿渡 洋
    Hiroshi
    Saruwatari

    Professor
    小山 翔一
    Shoichi
    Koyama

    Lecturer
    Augmented sound communication systems based on unsupervised optimization theory
    In many cases, acoustic signal processing deals with data that can be observed only just one time. This is because propagation of acoustic waves strongly depends on a sound field and spectral structures of the sound source. Thus, it is required to establish a framework that treats not “big data” but “small data.” For this reason, we are addressing blind (unsupervised) theories, e.g., independent low-rank matrix analysis and, sparse tensor decomposition. Also, we aim to build some applications of acoustic signal processing including a human-robot interface and universal communication-supporting systems.
    Mathematical analysis and sensibility quantification for non-linear signal processing
    Non-linear audio signal processing is applied to many tasks nowadays. In recent years, it is revealed that lower- and higher-order statistical space have a hysteresis property, which provides the fixed point of a human auditory impression. On the basis of this finding, we are pursuing the meaningful statistical estimation for humans and produce a new beneficial framework of signal processing.
    User-oriented and music signal processing
    We aim for developing high-quality music signal processing by applying machine learning theories to various multidimensional music data. Also, user-oriented systems for music signal analysis are addressed to contribute to built a new artistic production from the engineering view.
    Inverse problems for acoustic field
    We tackle with inverse problems for acoustic field, such as sound field imaging, analysis, source localization, and estimation of room acoustic parameters. We pursuit new methodologies with various approaches (optimization, machine learning, etc.) and develop systems to achieve these purposes.
    Signal processing for sound field recording, transmission, and reproduction
    We deal with a broad range of problems for sound field recording, transmission, and reproduction. By using these methodologies, we develop new systems for telecommunication, virtual reality, and so on.
    Augmented speech communication using speech synthesis and conversion
    Utilizing machine-learning-based speech synthesis and conversion, we realize augmented speech communication beyond differences among AI and human beings.

    Ayumu Matani

    Person

    Ayumu Matani
    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

    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 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.

     

    Suri7-Tanigawa

    Profile

    Shin-ichi Tanigawa
    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

    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 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.

     

     

    System Information Second Laboratory

    System Information Second Laboratory – Brain science based on system theory: brain function recording, brain function control – Laboratory homepage →
    Ayumu Matani
    Ayumu Matani

    Associate Professor
     
    Brain function recording
    For instance, I investigate whether or not humans still possess subconscious geomagnetic reception by EEG recording and behavioral experiments.
    Brain function control
    I try to control brain functions by attaching a negative impedance circuit on the scalp, so that it modulates the volume conduction of dendritic currents.

    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

    [Home Page]

    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
    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
    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.

     

    Mathematical Informatics 1st Laboratory

    Mathematical Cryptography Laboratory (Mathematical Informatics 1st Laboratory)
    – Let’s study the foundation of information security. –
    HomePage of Lab.→
    Tsuyoshi Takagi
    Tsuyoshi Takagi

    Professor
    Cryptography
    Modern cryptography has become one of the most important research fields in information technology. We aim at development and security evaluation of the next-generation cryptographic systems. In particular, we study post-quantum cryptography based on the mathematical problems (such as coding theory, lattice theory, multivariate polynomials, graph theory, etc), which are computationally intractable even in the era of quantum computing.
    Information Security
    With cryptography it is possible to construct many security protocols that become the basic infrastructure for secure communications such as SSL/TLS. These security protocols provide us with various security applications, for example, copyright protection, electronic voting, cryptocurrency, and so on. This research group is engaged in the development of new efficient cryptographic algorithms and implementation secure against physical attacks.