Author: keisu-editor

Ayumi Igarashi

Personal Information

Ayumi Igarashi
Ayumi Igarashi

Department of Mathematical Informatics, 
Graduate School of Information Science and Technology
Associate Professor

Room 330, Engineering Building 6, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656 
Tel: 03-5841-6549, (ext. 26549)
Fax:

E-mail：igarashi＠mist.i.u-tokyo.ac.jp

[Home Page]

 

Curriculum Vitae

March 2012 Bachelor of Policy and Planning Sciences, University of Tsukuba
March 2014 Master of Engineering, University of Tsukuba
March 2018 Ph.D in Computer Science, University of Oxford
April 2018 – March 2020 Postdoctoral Fellow of Japan Society for the Promotion of Science
April 2020 – September 2022 Assistant Professor, National Institute of Informatics
October 2022 – Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

Research Themes

I work on computational social choice. My main research focus is on designing fair resource allocation mechanisms that satisfy desirable fairness and efficiency properties. Applications include rent division among roommates, property division among family members, course assignment to students, and so on. I am also interested in developing multi-winner voting rules, where each group of voters has a fair influence on the outcome.

Selected Publications


Nawal Benabbou, Mithun Chakraborty, Ayumi Igarashi, Yair Zick, Finding Fair and Efficient Allocations for Matroid Rank Functions, ACM Transactions on Economics and Computation, 9 (4), pp. 1–41, 2021.

Vittorio Bilo, Ioannis Caragiannis, Michele Flammini, Ayumi Igarashi, Gianpiero Monaco, Dominik Peters, Cosimo Vinci, William S. Zwicker, Almost Envy-free Allocations with Connected Bundles, Games and Economic Behavior, 131, pp. 197–221, 2022.

Haris Aziz, Ioannis Caragiannis, Ayumi Igarashi, and Toby Walsh, Fair Allocation of Combinations of Indivisible Goods and Chores, The 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019, pp. 53–59.

Robert Bredereck, Edith Elkind, and Ayumi Igarashi, Hedonic Diversity Games, The 18th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2019, pp. 565–573.

 

Takeru Matsuda

Personal Information

Takeru Matsuda
Takeru Matsuda

Department of Mathematical Informatics, 
Graduate School of Information Science and Technology
Associate Professor

Room 344, Engineering Building 6, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656 
Tel: +81-3-5841-6910 (ext. 26910)
Fax:

E-mail：matsuda@mist.i.u-tokyo.ac.jp

[Home Page]

 

Curriculum Vitae

Mar. 2012 Bachelor degree from Department of Mathematical Engineering and Information Physics, Faculty of Engineering, The University of Tokyo
Mar. 2014 Master degree from Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
Mar. 2017 Ph. D. from Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
Apr. 2017 Assistant Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
Jun. 2020 Unit Leader, RIKEN Center for Brain Science, RIKEN
Oct. 2022 Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

Research Themes

1. Theoretical statistics: mathematical foundation of data analysis
2. Computational statistics: development of algorithms for data analysis
3. Applied statistics: modeling and analysis of data from various fields such as neuroscience

Main paper and books


Takeru Matsuda and William E. Strawderman. Estimation under matrix quadratic loss and matrix superharmonicity. Biometrika, 109, 503–519, 2022.

Takeru Matsuda, Masatoshi Uehara and Aapo Hyvarinen. Information criteria for non-normalized models. Journal of Machine Learning Research, 22(158):1–33, 2021.

Takeru Matsuda and Yuto Miyatake. Estimation of ordinary differential equation models with discretization error quantification. SIAM/ASA Journal on Uncertainty Quantification, 9, 302–331, 2021.

Takeru Matsuda. Statistical analysis of kimariji in competitive karuta (in Japanese). Japanese Journal of Applied Statistics, 49, 1–11, 2020.

Takeru Matsuda and Fumiyasu Komaki. Time series decomposition into oscillation components and phase estimation. Neural Computation, 29, 332–367, 2017.

 

Mathematical Data Science Lab. （Mathematics and Informatics Center）

Mathematical Data Science Lab. （Mathematics and Informatics Center） HomePage→
Yoshihiro Kanno
Yoshihiro Kanno
Professor		 Tomonari Sei
Tomonari Sei
Professor
Teppei Ogihara
Teppei Ogihara
Associate Professor
Mathematics of design optimization
Design optimization is the methodology that utilizes mathematical optimization to improve the rationality and sophistication of design processes in engineering. We mainly focus on developing mathematical modelings and numerical algorithms for solving diverse optimal design problems.

Statistical Modeling of Dependence
We develop statistical models and inference methods of dependence structure hidden in various data. The keywords are copula theory, directional statistics, optimal transport and algebraic statistics.

Statistical Analysis of Stochastic Processes
We study statistical methods for stochastic processes, especially parameter estimation methods such as maximum likelihood and Bayesian methods, and their asymptotic theories. We are also conducting applied research on high-frequency data for the Japanese and U.S. stock markets.

Lab. 1: Takayasu

Personal Information

Atsushi Takayasu
Atsushi Takayasu

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

Room 354,Engineering Building 6, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656 
Tel: 03-5841-6959 (ext. 26959)
Fax:

E-mail：takayasu-a@g.ecc.u-tokyo.ac.jp

[Home Page]

 

Biography

March 2012 Bachelor of Engineering, Department of Mathematical Engineering and Information Physics, Faculty of Engineering, The University of Tokyo
March 2014 Master of Science, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo
March 2017 Ph.D., Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo
April 2017 Research Associate, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
April 2020 Senior Researcher, Security Fundamental Laboratory, Cybersecurity Research Institute, National Institute of Information and Communications Technology
October 2021 Lecturer, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

Research Themes

We are conducting research on cryptography, which is a fundamental technology for the secure operation of the information society.
● Research on public-key cryptography, in particular, the construction and security proofs of post-quantum cryptosystems.
● Research on attack and solution algorithms for mathematical problems related to public key cryptosystems and their security.

Main paper and books

Atsushi Takayasu, Yao Lu, and Liqiang Peng. Small CRT-exponent RSA Revisited. Journal of Cryptology, Vol. 32, Issue 4, pp. 1337-1382, 2019.

Shuichi Katsumata, Takahiro Matsuda, and Atsushi Takayasu. Lattice-based Revocable (Hierarchical) IBE with Decryption Key Exposure Resistance. Proc. PKC 2019, LNCS 11443, pp. 441-471, Springer, 2019.

Atsushi Takayasu and Noboru Kunihiro. Partial Key Exposure Attacks on RSA: Achieving the Boneh-Durfee Bound. Theoretical Computer Science, Vol. 761, pp. 51-77, 2019.

 

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

 

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