Personal Information

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.

Personal Information

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.

Personal Information

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.

Profile

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.

Profile

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.

Profile

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)