東京大学工学部計数工学科 https://www.keisu.t.u-tokyo.ac.jp Just another WordPress site Tue, 05 Dec 2023 09:52:50 +0000 en-US hourly 1 https://wordpress.org/?v=6.4.3 CBS – Schmitt https://www.keisu.t.u-tokyo.ac.jp/en/2023/04/03/cbs-schmitt/ https://www.keisu.t.u-tokyo.ac.jp/en/2023/04/03/cbs-schmitt/#respond Mon, 03 Apr 2023 06:32:41 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=6029 Continue reading "CBS – Schmitt"]]>

Profile

Lukas Ian Schmitt
Schmitt

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

RIKEN Center for Brain Science

2-1 Hirosawa, Wako-shi, Saitama 351-0198
Tel:
Fax:

E-mail:lukas.schmitt@riken.jp

[Home Page]

CV

Jun. 2007 (Undergraduate) Received a bachelor of science degree in Brain and Cognitive Sciences from the Massachusetts Institute of Technology
Mar. 2014 (Graduate Research) Received a Doctoral Degree in Neuroscience from the Tufts University School of Medicine
Apr. 2014-Dec. 2017 (Postdoctoral Fellow) Conducted Postdoctoral Research at the New York University School of Medicine
Jan. 2018-Nov. 2019 (Research Scientist) Conducted Postdoctoral Research at the Massachusetts Institute of Technology
Jan. 2020-Present (Team Leader) Neuroscience

Research Themes

We aim to clarify how dynamic interactions between neuronal networks in the brain store and interpret information for the construction of internal models of the external world. To accomplish this, we continuously develop electrophysiological and optical techniques to measure and control neural activity in multiple brain regions during behavioral tasks that engage cognitive function. By analyzing the obtained data using dynamical systems and machine learning approaches as well as computational models of brain activity, we seek to unravel the computational principles underlying cognitive functions such as perception and inference.

Selected Publications

Schmitt LI, Wimmer RD, Nakajima M, Happ M, Mofakham S, Halassa MM. Thalamic amplification of cortical connectivity sustains attentional control (2017). Nature, 545:219-223. Doi: 10.1038/nature 22073. PMID: 28467827

Schmitt LI*, Wimmer RD*, Davidson TJ, Nakajima M, Deisseroth K, Halassa MM. Thalamic control of sensory selection in divided attention. (2015). Nature, 526:705-9. Doi: 10.1038/nature 15398. PMID: 26503050

Nakajima M, Schmitt LI, Halassa MM. Prefrontal Cortex Regulates Sensory Filtering through a Basal Ganglia-to-Thalamus Pathway. (2019) Neuron. Aug 7;103(3):445-458.e10. doi: 10.1016/j.neuron.2019.05.026. Epub 2019 Jun 12. PubMed PMID: 31202541; PubMed Central PMCID: PMC6886709.

Nakajima M*, Schmitt LI*, Feng G, Halassa MM. Combinatorial Targeting of Distributed Forebrain Networks Reverses Noise Hypersensitivity in a Model of Autism Spectrum Disorder. (2019) Neuron. Nov 6;104(3):488-500.e11. doi: 10.1016/j.neuron.2019.09.040. Epub 2019 Oct 21. PubMed PMID:31648899.

Wells MF*, Wimmer RD*, Schmitt LI, Feng G, Halassa MM. Thalamic reticular impairment underlies attention deficit in PtchD1Y/- mice. (2016). Nature, 532:58-63. Doi:10.1038/nature17427. PMID:27007844

Schmitt LI, Sims RE, Dale N, Haydon PG. Wakefulness Affects Synaptic and Network Activity by Increasing Extracellular Astrocyte-Derived Adenosine. (2012). Journal of Neuroscience, 32(13), 4417-4425. doi:10.1523/JNEUROSCI.5689-11.2012. PMID:22457491

 

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Ayumi Igarashi https://www.keisu.t.u-tokyo.ac.jp/en/2022/10/02/ayumi-igarashi/ https://www.keisu.t.u-tokyo.ac.jp/en/2022/10/02/ayumi-igarashi/#respond Sun, 02 Oct 2022 10:37:50 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=5835 Continue reading "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.

 

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Takeru Matsuda https://www.keisu.t.u-tokyo.ac.jp/en/2022/10/02/5830/ https://www.keisu.t.u-tokyo.ac.jp/en/2022/10/02/5830/#respond Sun, 02 Oct 2022 10:29:50 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=5830 Continue reading "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.

 

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Mathematical Data Science Lab. (Mathematics and Informatics Center) https://www.keisu.t.u-tokyo.ac.jp/en/2022/10/02/mathematical-data-science-lab-%ef%bc%88mathematics-and-informatics-center%ef%bc%89/ https://www.keisu.t.u-tokyo.ac.jp/en/2022/10/02/mathematical-data-science-lab-%ef%bc%88mathematics-and-informatics-center%ef%bc%89/#respond Sun, 02 Oct 2022 10:06:35 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=5820 Continue reading "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.
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Lab. 1: Takayasu https://www.keisu.t.u-tokyo.ac.jp/en/2021/10/05/lab-1-takayasu/ https://www.keisu.t.u-tokyo.ac.jp/en/2021/10/05/lab-1-takayasu/#respond Tue, 05 Oct 2021 11:25:52 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=5596 Continue reading "Lab. 1: Takayasu"]]>

Personal Information

Atsushi Takayasu
Atsushi Takayasu

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

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
December 2023 Associate Professor, 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.

 

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Yasushi Kawase https://www.keisu.t.u-tokyo.ac.jp/en/2020/10/09/yasushi-kawase/ https://www.keisu.t.u-tokyo.ac.jp/en/2020/10/09/yasushi-kawase/#respond Fri, 09 Oct 2020 12:02:37 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=5270 Continue reading "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.
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Mathematical Informatics Lab. 6 https://www.keisu.t.u-tokyo.ac.jp/en/2020/05/07/mathematical-informatics-lab-6/ https://www.keisu.t.u-tokyo.ac.jp/en/2020/05/07/mathematical-informatics-lab-6/#respond Thu, 07 May 2020 13:04:14 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=5168 Continue reading "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.
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Lab 6. Ryohei Hisano https://www.keisu.t.u-tokyo.ac.jp/en/2020/04/10/lab-6-ryohei-hisano/ https://www.keisu.t.u-tokyo.ac.jp/en/2020/04/10/lab-6-ryohei-hisano/#respond Fri, 10 Apr 2020 01:55:44 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=5114 Continue reading "Lab 6. Ryohei Hisano"]]>

Profile

Ryohei Hisano
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

March 2007 Bachelor degree from Department of Economics, Keio University
March 2010 Master’s degree from Graduate School of Economics, Hitotsubashi University
August 2013 D-MTEC (Dr. Sc. ETH Zürich) from ETH Zürich
Sept. 2013 Postdoctoral researcher, ETH Zürich, D-MTEC
Oct. 2013 Postdoctoral researcher, The National Institute of Informatics
April 2014 JSPS Research Fellow, Graduate School of Economics, The University of Tokyo
December 2015 Specially Appointed Research Associate, Graduate School of Information Science and Technology, The University of Tokyo
April 2020 Lecturer, Mathematics and Informatics Center, Graduate School of Information Science and Technology, The University of Tokyo

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.
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Kazuhiro Sato https://www.keisu.t.u-tokyo.ac.jp/en/2019/10/08/kazuhiro-sato/ https://www.keisu.t.u-tokyo.ac.jp/en/2019/10/08/kazuhiro-sato/#respond Tue, 08 Oct 2019 07:06:29 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=4819 Continue reading "Kazuhiro Sato"]]>

Profile

Kazuhiro SATO

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

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
December 2023 Associate Professor, 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.
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Yuki Izumida https://www.keisu.t.u-tokyo.ac.jp/en/2019/05/23/izumida/ https://www.keisu.t.u-tokyo.ac.jp/en/2019/05/23/izumida/#respond Thu, 23 May 2019 04:46:18 +0000 https://www.keisu.t.u-tokyo.ac.jp/?p=4709 Continue reading "Yuki Izumida"]]>

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

March 2011 Doctor of Philosophy in the field of Quantum and Condensed-Matter Physics at the Graduate School of Science, Hokkaido University
April 2010 Research Fellowship for Young Scientists (DC2) (Hokkaido University)
April 2011 Research Fellowship for Young Scientists (PD) (The University of Tokyo)
April 2012 Project Collaborative Researcher, Graduate School of Science, The University of Tokyo
May 2012 Project Researcher, Graduate School of Science, The University of Tokyo
July 2012 Project Research Fellow, Center for Simulation Sciences, Ochanomizu University
April 2013 Research Fellowship for Young Scientists (PD) (Ochanomizu University)
April 2015 Assistant Professor, Graduate School of Information Science, Nagoya University
April 2017 Assistant Professor, Graduate School of Informatics, Nagoya University
May 2019 Lecturer, Graduate School of Frontier Sciences, The University of Tokyo

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

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