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CBS – Schmitt

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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


 

Ayumi Igarashi

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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

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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

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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

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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）

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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

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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

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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

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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

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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

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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

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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

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Neuroinformatics and Computational Neuroscience (RIKEN)
Benucci
Andrea Benucci
Adjunct Professor		 Toyoizumi
Taro Toyoizumi
Adjunct Professor
Lukas Ian Schmitt
Lukas Ian Schmitt Associate 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.