Year: 2018

Nonlinear Dynamics Lab

Posted on by suuri8
Nonlinear Physics Lab. (Department of Complexity Science and Engineering）
 HomePage of Lab →

Modeling and analysis of dynamical systems

Hiroshi
Kori
Professor
Yuki
Izumida
Lecturer

To understand natural, biological, and artificial systems, we perform mathematical modeling and analysis. Moreover, closely collaborating with experimentalists of various fields, we try to solve problems related to our lives. 

Modeling and development of general theories
By constructing simple models that describe complex dynamical phenomena, we try to understand, predict, and control such phenomena. Moreover, through the generalizing and abstraction of problems, we try to construct general theories. Examples of our subjects include biological rhythms, locomotion, hydrodynamic phenomena, power grids, transportation networks, traffic networks, pattern formation in biological and chemical systems, social systems, neural networks.

Collaboration with experimentalists
To solve problems closely related to our lives, we collaborate with researchers of various disciplines such as engineering and biology. Our roles are to provide theoretical ideas, to analyze and interpret experimental data, and to propose new experiments. 

Keywords
nonlinear phenomena, oscillations, synchronization, fluctuation, complex networks, control, optimization, biological rhythms, circadian rhythms, locomotion, biological physics

Hiroshi KORI

Posted on by suuri8

Profile

Hiroshi KORI

Graduate School of Frontier Sciences
The University of Tokyo
Professor

5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken 277-8561
Tel: +81 4 7136 3936

E-mail：kori@k.u-tokyo.ac.jp

[Personal Site]

Biography

March 2003 Doctor of Physics, Department of Physics, Graduate School of Science, Kyoto University
April 2004 Research Fellow of Max Planck Society (Fritz Haber Institute
March 2005 Alexander von Humboldt Research Fellow (Fritz Haber Institute)

April 2006

 Research Fellow, Department of Mathematics, Hokkaido University
March 2008 Assistant Professor, Ochadai Academic Production, Ochanomizu University
April 2012 Associate Professor, Affiliation: Department of Computer Sciences, Ochanomizu University
September 2018 Professor, Graduate School of Frontier Sciences, The University of Tokyo

Research Topics

I am working on various dynamical systems, such as
– Synchronization
– Complex networks
– Biological rhythms, circadian rhythms, jet lag
– Locomotion
– Power grids, transportation networks
– Self-organization, pattern formation
– Chemical reactions
– Micro-Macro links in nonlinear non-equilibrium systems

 

Selected Publications

  • H. Kori, Y. Yamaguchi, H. Okamura: “Accelerating recovery from jet lag: prediction from a multi-oscillator model and its experimental confirmation in model animals”, Scientific Reports 7, 17466 (2017)
  • H. Kori, Y. Kuramoto, S. Jain, I.Z. Kiss, J.L. Hudson: “Clustering in Globally Coupled Oscillators Near a Hopf Bifurcation: Theory and Experiments”, Phys. Rev. E 89, 062906 (2014)
  • Y. Yamaguchi, H. Kori, H. Okamura et al: “Mice Genetically Deficient in Vasopressin V1a and V1b Receptors Are Resistant to Jet Lag”, Science 342,  85 (2013)
  • I. Imayoshi, H. Kori, R. Kageyama et al.: “Oscillatory Control of Factors Determining Multipotency and Fate in Mouse Neural Progenitors”, Science 342, 1203 (2013)
  • I.Z. Kiss, C.G. Rusin, H. Kori, J.L. Hudson: “Engineering Complex Dynamical Structures: Sequential Patterns and Desynchronization”, Science 316, 1886 (2007)

Mathematical Informatics of Social Complex Systems

Posted on by keisu-admin
Laboratory for Mathematical Informatics of Social Complex Systems
-Language , Communication, and Financial Markets-
(Research Center for Advanced Science and Technology) 		Webpage of Lab→
Kumiko Tanaka-Ishii
Kumiko Tanaka-Ishii
Professor

We explore the universal properties underlying large-scale social
systems through mathematical models derived by computing with big data
obtained from large-scale resources. Using these models, we explore
new ways of engineering to aid human social activities.

1. Analysis of large-scale social systems by applying complex systems theory
Common scaling properties are known to hold across various large-scale social systems. Using real, large-scale data, we study the nature of these properties, from viewpoints such as complexity, degree of fluctuation, and self-similarity, and construct a mathematical model that explains them.

 

2. Deep/Machine learning methods for complex systems
We discuss the potential and limitations of deep learning and other machine learning techniques with respect to the nature of complex systems, and we study directions for improvement. Moreover, we explore unsupervised and semi-supervised methods for state-of-the-art learning techniques.

 

3. Mathematical informatics across language, financial markets, and communication
We explore common universal properties underlying language, finance, and communication, through computing with various kinds of large-scale data, and we apply our understanding of those properties to engineering across domains. For example, we study financial market analysis by using blogs and other information sources, and we simulate information spread on a large-scale communication network.

seisanken – omi

Posted on by keisu-editor

Profile

Takahiro Omi（近江 崇宏）
近江 崇宏

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

Aihara lab., Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
Tel: +81-3-5452-6697 (Ext. 56697)
Fax:

E-mail：omi@sat.t.u-tokyo.ac.jp

Curriculum Vitae

March 2007 Bachelor of Science from Faculty of Science, Kyoto University
March 2009 Master of Science from Department of Physics, Graduate School of Science, Kyoto University
March 2012 Ph.D. in Science from Department of Physics, Graduate School of Science, Kyoto University
April 2012 Researcher, Japan Science and Technology
April 2013 Japan Society for the Promotion of Science Fellowship for Young Scientists
April 2016 Project Research Associate, Institute of Industrial Science, The University of Tokyo
April 2018 Project Associate Professor, Institute of Industrial Science, The University of Tokyo

Research Themes

Our main topics is time-series analysis. We especially focus on the statistical analysis of point process data, which describe events that occur irregularly in time. Our research includes

(1) the development of estimation and forecast method based on Bayesian statistics and

(2) its application to earthquake, economic, and social data.

Selected Publications

T. Omi, Y. Hirata, and K. Aihara,

“Hawkes process model with a time-dependent background rate and its application to high-frequency financial data”,

Physical Review E 96, 012303 (2017).


T. Omi, Y. Ogata, Y. Hirata, and K. Aihara,

“Forecasting large aftershocks within one day after the main shock”,

Scientific Reports 3, 2218 (2013).


T. Omi and S. Shinomoto,

“Optimizing time histograms for non-Poissonian spike trains”,

Neural Computation 23, 3125 (2011).

Yoshihiro Kanno

Posted on by suuri5

教員紹介

Yoshihiro Kanno
Yoshihiro Kanno

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 435
Tel: +81-3-5841-6913 (ext. 26913)
Fax:+81-3-5841-6886

E-mail： kanno＠mist.i.u-tokyo.ac.jp

[Home Page]

Education and Employment

March 1998 B. Eng., Kyoto University
March 2000 M. Eng., Kyoto University
September 2002 Dr. Eng., Kyoto University
March 2004 Assistant Professor, Department of Urban and Environmental Engineering, Kyoto University
May 2006 Assistant Professor, Department of Mathematical Informatics, The University of Tokyo
September 2008 Associate Professor, Department of Mathematical Informatics, The University of Tokyo
April 2015 Associate Professor, Materials and Structures Laboratory, Tokyo Institute of Technology
April 2016 Associate Professor, Laboratory for Future Interdisciplinary Research of Science and Technology, Tokyo Institute of Technology
October 2017 Professor, Mathematics and Informatics Center, The University of Tokyo

Research Interests

Modeling and algorithms of mathematical optimization and their applications to applied mechanics and structural design

  • Continuous optimization and applied mechanics: convex optimization, complementarity, duality and their applications to structural optimization, contact mechanics, plasticity, etc.
  • Robust optimization and its applications: Optimization with uncertain data, robust optimization of structures, robustness evaluation of uncertain systems, etc.

Selected Publications

Y. Kanno, “A fast first-order optimization approach to elastoplastic analysis of skeletal structures,” Optimization and Engineering, 17, 861–896 (2016).
Y. Kanno, “Nonsmooth Mechanics and Convex Optimization,” CRC Press, Boca Raton (2011).
Y. Kanno, J. A. C. Martins, A. Pinto da Costa, “Three-dimensional quasi-static frictional contact by using second-order cone linear complementarity problem,” International Journal for Numerical Methods in Engineering, 65, 62–83 (2006).

Mathematical Programming Laboratory

Posted on by suuri5
Mathematical Programming Laboratory（Mathematical Informatics 5th Laboratory）
– Resolve “troubles” of the world – 		HomePage of Lab.→
Akiko Takeda
Akiko　Takeda
Professor		 Kazuhiro Sato
Kazuhiro Sato
Lecturer
Operations Research（OR）
It is a scientific technique that builds mathematical models and finds their solutions by using computers for solving real problems. In particular, we focus on modeling as a mathematical optimization problem and developing algorithms to solve the problem. The scope of application of OR is diverse and we are conducting research to solve real-world problems in the fields of structure design, energy system, financial engineering, machine learning.
Efficient algorithms for continuous optimization and thier applications to real-world problems
Problems in real world often result in large scale, nonlinear, nonconvex continuous optimization problem. Also, in a situation where robustness against uncertainty (variation) of data is required, a model called a robust optimization problem may be useful. We aim to efficiently solve such optimization problems and contribute to real world problem solving.

Collaborative Research Center for Innovative Mathematical Modeling

Posted on by seimei

 

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

Posted on by seimei

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.

    Posted on by system1
    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

    Posted on by system2

    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.