Yasushi Kawase

Profile

Yasushi Kawase
Yasushi Kawase

Project Associate Professor
Department of Mathematical Informatics, Graduate School of Information Science and Technology
RIISE: Research Institute for an Inclusive Society through Engineering

Room 436, Engineering Bldg. 6, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656
Tel: 03-5841-0698 (ext. 20698)
Fax: 03-5841-0698

E-mail:kawase@mist.i.u-tokyo.ac.jp

[Home page]

Biography

March 2009 Bachelor of Engineering, Department of Mathematical Engineering and Information Physics, School of Engineering, The University of Tokyo
March 2011 Master of Information Science and Technology, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
March 2014 Doctor of Information Science and Technology, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
April 2014 Assistant Professor, Department of Social Engineering, Graduate School of Decision Science and Technology, Tokyo Institute of Technology
April 2016 Assistant Professor, Department of Industrial Engineering and Economics, School of Engineering, Tokyo Institute of Technology
October 2020 Project Associate Professor, Graduate School of Information Science and Technology, The University of Tokyo

Research Themes

(1) Discrete optimization: Design of algorithms for online optimization problems, robust optimization problems, etc.

(2) Algorithmic game theory: Design and analysis of mechanisms in strategic behavior.

Selected Publications

– Yasushi Kawase and Atsushi Iwasaki: Approximately Stable Matchings with General Constraints, Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS2020), Pages 602–610, May 2020.
– Yasushi Kawase and Hanna Sumita: Randomized Strategies for Robust Combinatorial Optimization, Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI2019), Pages 7876–7883, January 2019
– Yasushi Kawase, Kazuhisa Makino, and Kento Seimi: Optimal Composition Ordering Problems for Piecewise Linear Functions, Algorithmica, Vol. 80, Issue 7, Pages 2134–2159, July 2018
– Xin Han, Yasushi Kawase, and Kazuhisa Makino: Online Unweighted Knapsack Problem with Removal Cost, Algorithmica, Vol. 70, Pages 76–91, September 2014.

Mathematical Informatics Lab. 6

Mathematical Informatics (Lab. 6) HomePage→
山西 健司
Kenji Yamanishi

Professor
鈴木 大慈
Taiji Suzuki

Associate Professor
久野 遼平
Ryohei Hisano

Lecturer
Information-theoretic learning theory/Statistical learning theory
“What are the possibility and limitation of machine learning?” We take information-theoretic and statistical approaches to answer this question. As for information-theoretic learning theory, we study a unifying methodology for model selection, representation learning, change detection, high-dimensional sparse learning, etc. on the basis of the minimum description length principle. As for statistical learning, we study new algorithm designs and theoretical analysis of deep learning and kernel methods on the basis of statistical theory. We also develop new optimization methods to run the machine learning algorithms efficiently on the big data.

Data Science Foundation
We study methodologies for knowledge discovery from big data (anomaly detection, network mining, embedding, etc.) Specifically we are interested in discovering deep knowledge from latent spaces. We aim at building a new field called “Symptomatics”, in which we detect signs of latent changes in future from data streams.

Data Science Applications
We study effective data science methodologies by applying machine learning and data mining technologies to real complex data. The applications include economics, financial data analysis, medical data analysis, marketing, SNS data analysis, failure detection, spatial data mining, security, etc. We often collaborate with industrial companies to solve real data science problems.

Kazuhiro Sato

Profile

Kazuhiro SATO

Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
Lecturer

Room 434, 7-3-1 6 Hongo, Bunkyo-ku, Tokyo 113-8656
Tel: +81-03-5841-6934

E-mail:kazuhiro@mist.i.u-tokyo.ac.jp

[Personal Site]

Biography

March 2009 Bachelor of Engineering from Undergraduate School of Informatics and Mathematical Science, Kyoto University
March 2011 Master of Informatics from Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University
March 2013 Ph. D. in Informatics from Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University
April 2014 Project Researcher, Graduate School of Informatics, Kyoto University
April 2017 Specially Appointed Assistant Professor, Kitami Institute of Technology
April 2018 Assistant Professor, Kitami Institute of Technology
October 2019 Lecturer, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

Research Topics

I am especially interested in solving control systems problems using methods of different fields such as optimization and machine learning. The following three topics are main research subjects.

  1. Applications of optimization theory to control systems theory: We study what control systems problems can be solved using Riemannian optimization, proximal algorithm, submodular optimization, and so on.
  2. Applications of control systems theory to optimization theory: We study what optimization problems can be efficiently solved using control systems theories such as hybrid systems, passivity, and so on.
  3. Data-driven modeling for controlling systems: We study efficient modeling methods for controlling systems from time series data using optimization, machine learning, numerical analysis, and so on.

 

Selected Publications

  • K. Sato:Riemannian optimal model reduction of linear port-Hamiltonian systems, Automatica, Vol. 98, pp. 428–434, 2018.
  • K. Sato and H. Sato: Structure preserving H^2 optimal model reduction based on Riemannian trust-region method, IEEE Transactions on Automatic Control, Vol. 63, No. 2, pp. 505-511, 2018.
  • K. Sato: Riemannian optimal control and model matching of linear port-Hamiltonian systems, IEEE Transactions on Automatic Control, Vol. 62, No. 12, pp. 6575-6581, 2017.

CBS – Benucci

Profile

Andrea Benucci
Benucci

Adjunct Professor,
Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo

RIKEN Center for Brain Science, Team Leader

2-1 Hirosawa, Wako-shi, Saitama 351-0198
Tel: +81-48-467-5203
Fax:

E-mail:andrea.benucci@riken.jp

[Home Page]

略歴

Nov. 1998 B.S., Physics, University of Padova, IT
Sept. 1999 M.S., Computational Neuroscience, International School for Advanced Studies, IT
Nov. 2003 Ph.D., Neuroscience, ETH/University of Zurich, CH
July 2003 Postdoctoral Fellow, Smith-Kettlewell Eye Research Institute, US
Sept. 2005 Research Associate, Smith-Kettlewell Eye Research Institute, US
Sept. 2008 Senior Research Associate, University College London, UK
Sept. 2013 Team Leader, RIKEN Brain Science Institute, JP
April 2018 Team Leader, RIKEN Center for Brain Science, JP

Research Themes

Computations in biological neural networks, in particular linear and non-linear analyses of large-scale neuronal recordings.

My research aims at linking neural architectures to the underlying computations. To do so, I integrate experimental methods for all-optical dissection of neuronal circuits with large-scale dynamical network models based on artificial neural networks (aNNs). The connectivity architecture of aNNs closely mirrors that of biological neural networks, thus representing an effective theoretical framework to unify computational, algorithmic, and implementation levels of analysis.

Selected Publications

Aoki, R., Tsubota, T., Goya, Y., Benucci, A., An automated platform for high-throughput mouse behavior and physiology with voluntary head-fixation. Nature Comms., 8:1196, (2017)

Benucci, A., Saleem, A.B., Carandini, M. Adaptation maintains population homeostasis in primary visual cortex. Nature Neurosci., Jun; 16(6):724-9, (2013)

Pearson R.A., Barber A.C., Rizzi M., Xue T., West E.L., Duran Y., Smith A.J., Chuang J.Z., Azam S.A., Luhmann U.F.O., Benucci A., Sung C.H., Carandini M., Yau K.W., Sowden J.C., Ali R.R. Restoration of vision after transplantation of photoreceptors. Nature, 485(7396):99-103, (2012)

Benucci, A., Ringach, D.L., Carandini, M. Coding of stimulus sequences by population responses in visual cortex. Nature Neurosci., 12(10):1317-24, (2009)

 

Neuroinformatics and Computational Neuroscience

Neuroinformatics and Computational Neuroscience (RIKEN)
Benucci
Andrea Benucci

Adjunct Professor
Toyoizumi
Taro Toyoizumi

Adjunct Professor
Prediction and verification of decision by sense
We are interested in computational principles of brains from the viewpoint of decision by sense. We use artificial neural networks as models of neuronal circuits. In particular, by using state-of-the-art techniques of imaging and optical genetics for mouses, we verify the prediction derived from the models by experiments.
 
Computational Neuroscience
We study the mechanism how brain circuits adapt to and learn from the environment. By combining theoretical techniques from statistical physics and information theory and analysis of experimental data, we understand how the information processing of brains changes by learning, and find out the fundamental principle explaining the changes.

CBS – Toyoizumi

Profile

Taro Toyoizumi(豊泉 太郎)
toyoizumi

Adjunct Professor,
Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo

RIKEN Center for Brain Science, Team Leader

2-1 Hirosawa, Wako-shi, Saitama 351-0198
Tel: +81-48-467-9644
Fax: +81-48-467-9670

E-mail:taro.toyoizumi@riken.jp

[Home Page]

Curriculum Vitae

Mar. 2001 Graduated from the Department of Physics, School of Science, Tokyo Institute of Technology
Mar. 2003 Graduated from the Master Course of the Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo
Mar. 2006 Graduated from the Doctor Course of the Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo
Apr. 2006 – Feb. 2008 Japan Society for the Promotion of Science Postdoctoral Fellow (RIKEN Brain Science Institute, Center for Theoretical Neuroscience, Columbia University)
Mar. 2008 – Feb. 2010 The Robert Leet and Clara Guthrie Patterson Trust Postdoctoral Fellow (Center for Theoretical Neuroscience, Columbia University)
Apr. 2010 – Mar. 2011 Special Postdoctoral Researcher, RIKEN Brain Science Institute
Apr. 2011 – Mar. 2018 Team Leader, RIKEN Brain Science Institute
Apr. 2018 Team Leader, RIKEN Center for Brain Science
Apr. 2019 Adjunct Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo

Research Themes

Computational Neuroscience, Theory of Neural Adaptation using Statistical Physics and Information Theory Tools

Our research is in the field of Computational Neuroscience. Computer models are used to study how information is processed in the brain and how the brain circuits adapt to and learn from the environment. We employ analytical techniques from statistical physics and information theory to investigate key functional properties for neuronal circuits. We use these techniques to reduce diverse experimental findings into a few core concepts that robustly explain the phenomena of interest. We are particularly interested in activity-dependent forms of plasticity in the brain, which are known to have large impacts on learning, memory, and development. With the aid of mathematical models, we seek a theory that unites the cellular level plasticity rules and the circuit level adaptation in different brain areas and animal species. Efficacy of neurons to represent and retain information is estimated from the structure and behavior of resulting circuits.

Selected Publications

Isomura T and Toyoizumi T.: “Error-Gated Hebbian Rule: A Local Learning Rule for Principal and Independent Component Analysis” Scientific Reports , 8, 1835 (2018), doi:10.1038/s41598-018-20082-0

Buckley C L and Toyoizumi T.: “A theory of how active behavior stabilizes neural activity: neural gain modulation by closed-loop environmental feedback” PLOS Computational Biology , 14, e1005926 (2018), doi: 10.1371/journal.pcbi.1005926

Kuśmierz Ł and Toyoizumi T.: “Emergence of Lévy walks from second-order stochastic optimization” Physical Review Letters, 119, 250601 (2017), doi: 10.1103/PhysRevLett.119.250601

Tajima S, Mita T, Bakkum D, Takahashi H, and and Toyoizumi T.: “Locally embedded presages of global network bursts” Proc. Natl. Acad. Sci, 114, 9517-9522 (2017), doi: 10.1073/pnas.1705981114

Huang H and Toyoizumi T.: “Clustering of neural code words revealed by a first-order phase transition” Physical Review E, 93, 062416 (2016), doi: 10.1103/PhysRevE.93.062416

Shimazaki H, Sadeghi K, Ishikawa T, Ikegaya Y, and Toyoizumi T.: “Simultaneous silence organizes structured higher-order interactions in neural populations.” Sci Rep, 5, 9821 (2015), doi: 10.1038/srep09821

Toyoizumi T, Kaneko M, Stryker MP, and Miller KD.: “Modeling the dynamic interaction of Hebbian and homeostatic plasticity” Neuron, 84(2), 497-510 (2014), doi: 10.1016/j.neuron.2014.09.036

Toyoizumi T, Miyamoto H, Yazaki-Sugiyama Y, Atapour N, Hensch TK, and Miller KD.: “A theory of the transition to critical period plasticity: inhibition selectively suppresses spontaneous activity” Neuron, 80(1), 51-63 (2013), doi: 10.1016/j.neuron.2013.07.022

Toyoizumi T and Abbott LF.: “Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime” Physical Review, E 84(5), 051908 (2011), doi: 10.1103/PhysRevE.84.051908

Toyoizumi T, Aihara K, and Amari S.: “Fisher information for spike-based population decoding.” Phys Rev Lett, 97(9), 98102 (2006), doi: 10.1103/PhysRevLett.97.098102

Toyoizumi T, Pfister JP, Aihara K, and Gerstner W.: “Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission.” Proc Natl Acad Sci U S A, 102(14), 5239-44 (2005), doi: 10.1073/pnas.0500495102

 

Professor Kantaro Fujiwara

Profile

Kantaro Fujiwara
Kantaro Fujiwara
Project Associate Professor
International Research Center for Neurointelligence, The University of Tokyo
Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
 
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Tel: +81-3-5841-8247 (Ext. 28247)
Fax:
 
E-mail:fujiwara@mist.i.u-tokyo.ac.jp
 
[Home page]

C.V.

March 2008 Graduated from the Doctor Course of the Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
April 2008 Postdoctoral Fellow of Japan Society for the Promotion of Science (Institute of Industrial Science, The University of Tokyo)
April 2011 Assistant Professor at the Division of Mathematics, Electronics and Informatics, Graduate School of Science and Engineering, Saitama University
April 2014 Assistant Professor at the Department of Management Science, Faculty of Engineering, Tokyo University of Science
April 2018 Project Associate Professor at the International Research Center for Neurointelligence, The University of Tokyo

Research Themes

The main topics are computational neuroscience and data analysis of neural systems.
1. Computational Neuroscience
Mathematical modeling of neural networks. Modeling various neuronal phenomena such as learning and adaptation.
2. Data Analysis of Neural Systems
Establishing mathematical theories and novel analysis method of neuronal data.
3. Biological Information Processing
Mathematical modeling of pancreatic beta cell and diabetes.

Selected Publications

– R. Nomura , Y-Z Liang, K. Morita, K. Fujiwara and T. Ikeguchi, Threshold-varying integrate-and-fire model reproduces distributions of spontaneous blink intervals, PLOS ONE 13, 10, e0206528 (2018)
– T. Kobayashi, Y. Shimada, K. Fujiwara and T. Ikeguchi, Reproducing Infra-Slow Oscillations with Dopaminergic Modulation, Scientific Reports, 7, 2411 (2017)
– H. Ando and K. Fujiwara, Numerical analysis of bursting activity in an isolated pancreatic β-cell model, Nonlinear Theory and its Applications, 7, pp. 217-225 (2016)
– K. Fujiwara, H. Suzuki, T. Ikeguchi and K. Aihara, Method for analyzing time-varying statistics on point process data with multiple trials, Nonlinear Theory and its Applications, 6, pp. 38-46 (2015)

seisanken – omi

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).