Personal Information

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

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.

Personal Information

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

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.

Personal Information

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

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.

Profile

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

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.

Profile

Ryohei Hisano

Mathematics and Informatics Center, Graduate School of Information Science and Technology, The University of Tokyo
Lecturer

Room 214B, Engineering Bldg. 12, 2-5-37, Ikenohata, Taito-ku, Tokyo 110-0008
Tel:

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

[Personal Site]

Biography

Research Topics

My research interests lie in both empirical research and statistical model building of social and economic big data. On the empirical research side, my research has mainly focused on analyzing datasets primarily in finance and macroeconomics domain (financial markets, blockchain, news text, financial statements, firm networks, sales of products), but this does not mean that I am solely interested in economics. For the statistical modeling side, my focus is on developing models that take into account various characteristics and empirical regularities found in the economy and utilizing information from multiple sources in the form of a heterogeneous information network. For the latter model building research, I mainly develop network mining (simple, temporal, heterogeneous information network) and text mining methods.
By combining the two research topics, my goal is to model complex issues in society (e.g., propagation and mitigation of shocks, hidden industrial block structure, aggregate fluctuation, matching among firms, network formation, bubbles, crashes, financial statements, systemic risks, the velocity of money, news events and reliability of information) to not only contribute to academic research but better understand risks and accelerate evidence-based policymaking.

Selected Publications

Ryohei Hisano, Didier Sornette, Takayuki Mizuno , “Prediction of ESG compliance using a heterogeneous information network”, Journal of Big Data 7, 22, 2020.

Ryohei Hisano, “Learning Topic Models by Neighborhood Aggregation”, IJCAI 2019, Macao, China, Aug 10 -16, 2019.

Ryohei Hisano, “Semi-supervised Graph Embedding Approach to Dynamic Link Prediction”, Complenet 2018, Northeastern University, Boston, Match 4-8, 2018. In: Sean Cornelius, Kate Coronges, Bruno Gonçalves, Roberta Sinatra, Alessandro Vespignani (Eds.), Complex Networks IX. Springer Proceedings in Complexity, page 109-121, 2018.

Ryohei Hisano, Tsutomu Watanabe, Takayuki Mizuno, Takaaki Ohnishi, Didier Sornette, “The gradual evolution of buyer-seller networks and their role in aggregate fluctuations”, Applied Network Science, Vol 2, 9, 2017.

Ryohei Hisano, “A new approach to building the interindustry input-output table using block estimation techniques”, 2016 IEEE International Conference on Big Data (Big Data), Workshop Application of Big Data for Computational Social Science, 5-8 Dec. 2016.

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

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.

Profile

Yuki IZUMIDA

Graduate School of Frontier Sciences
The University of Tokyo
Lecturer

5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken 277-8561
Tel: +81-4-7136-3934

E-mail：izumida@k.u-tokyo.ac.jp

[Personal Site]

Biography

Research Topics

I am studying fundamental aspects of complex and dynamical systems described by nonequilibrium statistical mechanics and nonlinear dynamics through mathematical modeling of specific systems in physics, engineering, and biology.
The research subjects I have being working on include theory of thermodynamic efficiency of nonequilibrium heat engines, dynamical modeling of a low-temperature-differential Stirling engine and elucidation of its rotational mechanism, and
construction of energetics of synchronization in coupled oscillators.

Selected Publications

• Y. Izumida: “Nonlinear dynamics analysis of a low-temperature-differential kinematic Stirling heat engine”, EPL(Europhysics Letters) 121, 50004 (2018)
• Y. Izumida, H. Kori, U. Seifert: “Energetics of synchronization in coupled oscillators rotating on circular trajectories”, Phys. Rev. E 94, 052221 (2016)
• Y. Izumida, K. Okuda: “Work Output and Efficiency at Maximum Power of Linear Irreversible Heat Engines Operating with a Finite-Sized Heat Source”,
  Phys. Rev. Lett. 112, 180603 (2014)
• Y. Izumida, K. Okuda: “Onsager coefficients of a finite-time Carnot cycle”,
  Phys. Rev. E 80, 021121 (2009)
• Y. Izumida, K. Okuda: “Molecular kinetic analysis of a finite-time Carnot cycle”,
  EPL(Europhysics Letters) 83, 60003 (2008)

Profile

Satoru Iwata　

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

Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
Tel: +81 3 5841 7430 (ext. 27430) 

E-mail：iwata＠mist.i.u-tokyo.ac.jp

[ Personal Site ]

Biography

Research Topics

Solving Fundamental Problems in Mathematical Engineering
– Discrete Optimization: Design and Analysis of Efficient Algorithms on Matroids and Submodular Functions
– Discrete Mathematical Engineering: Engineering Applications of Discrete Optimization Methods (Systems Analysis and Chemoinformatics)

Selected Publications

S. Iwata and M. Takamatsu: Index minimization of differential-algebraic equations in hybrid analysis for circuit simulation, Mathematical Programming, 103 (2010), 105-121.

J. F. Geelen, S. Iwata, and K. Murota: The linear delta-matroid parity problem, Journal of Combinatorial Theory, B88 (2003), 377-398.

S. Iwata, L. Fleischer, and S. Fujishige: A combinatorial strongly polynomial algorithm for minimizing submodular functions, Journal of the ACM, 48 (2001), 761-777.

Profile

Andrea 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

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)

Profile

Taro Toyoizumi（豊泉 太郎）

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

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