Curriculum Vitae
Takeru Matsuda
June 1, 2020

Education• March 2012: Bachelor of Engineering, Department of Mathematical Engineering andInformation Physics, The University of Tokyo• March 2014: Master of Information Science and Technology, Department of Mathematical Informatics, The University of Tokyo• March 2017: Doctor of Information Science and Technology, Department of Mathematical
Informatics, The University of Tokyo

Academic Experience• April 2014 – March 2017: JSPS Research Fellow (DC1)• April 2017 – May 2020: Assistant Professor, Department of Mathematical Informatics,The University of Tokyo• June 2017 – March 2018: Visiting Researcher, RIKEN Brain Science Institute• April 2018 – May 2020: Visiting Researcher, RIKEN Center for Brain Science• June 2020 – present: Statistical Mathematics Unit Leader, RIKEN Center for Brain
ScienceAwards• March 2012: Dean’s award from Faculty of Engineering, The University of Tokyo• March 2017: Dean’s award from Graduate School of Information Science and Technology,
The University of TokyoPublications• T. Matsuda and W. E. Strawderman. Predictive density estimation under the Wasserstein loss.Journal of Statistical Planning and Inference, Vol. 210, pp. 53–63, 2021.

1• M. Uehara, T. Matsuda and J. K. Kim. Imputation estimators for unnormalized models with missing data. Proceedings of the 23rd International Conference on Artificial
Intelligence and Statistics (AISTATS 2020), 2020.
• W. Xu and T. Matsuda. A Stein goodness-of-fit test for directional distributions. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics(AISTATS 2020), 2020.
• M. Uehara, T. Kanamori, T. Takenouchi and T. Matsuda. A unified estimation framework for unnormalized models with statistical efficiency. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), 2020.
• T. Ohki∗ , T. Matsuda∗ , A. Gunji, Y. Takei, R. Sakuma, Y. Kaneko, M. Inagaki, T.
Hanakawa, K. Ueda, M. Fukuda and K. Hiraki. Timing of phase amplitude couplingin the temporal pole is essential for neuronal and functional maturation of audiovisual
integration in adolescence. Brain and Behavior, e01635, 2020.
• T. Matsuda. Statistical analysis of kimariji in competitive karuta (in Japanese). Japanese
Journal of Applied Statistics, Vol. 49, pp. 1–11, 2020.
• T. Matsuda and A. Takemura. Game-theoretic derivation of upper hedging prices of multivariate contingent claims and submodularity. Japan Journal of Industrial and Applied
Mathematics, Vol. 37, pp. 213–248, 2020.
• T. Matsuda and A. Hyvärinen. Estimation of Non-Normalized Mixture Models. 22ndInternational Conference on Artificial Intelligence and Statistics (AISTATS 2019).
• T. Matsuda and F. Komaki. Empirical Bayes matrix completion. Computational Statistics & Data Analysis, Vol. 137, pp. 195–210, 2019.
• T. Matsuda and W. E. Strawderman. Improved loss estimation for a normal meanmatrix. Journal of Multivariate Analysis, Vol. 169, pp. 300–311, 2019.
• Y. Maruyama, T. Matsuda and T. Onishi. Harmonic Bayesian prediction under alphadivergence. IEEE Transactions on Information Theory, Vol. 65, pp. 5352–5366, 2019.
• Y. T. Chang, T. Matsuda and W. E. Strawderman. A Note on Improving on a Vector of Coordinate-wise Estimators of Non-negative Means via Shrinkage. Statistics &Probability Letters, Vol. 153, pp. 143–150, 2019.
• T. Matsuda and F. Komaki. Time series decomposition into oscillation components andphase estimation. Neural Computation, Vol. 29, pp. 332–367, 2017.
• T. Matsuda and F. Komaki. Multivariate time series decomposition into oscillation
components. Neural Computation, Vol. 29, pp. 2055–2075, 2017.
• T. Matsuda, K. Kitajo, Y. Yamaguchi and F. Komaki. A point process modeling approach for investigating the effect of online brain activity on perceptual switching. NeuroImage, Vol. 152, pp. 50–59, 2017.
• T. Koyama, T. Matsuda and F. Komaki. Minimax estimation of quantum states basedon the latent information priors. Entropy, Vol. 19, 618, 2017.

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• M. Tsuji, T. Kawasaki, T. Matsuda, S. Gojo and J. Takeuchi. Sexual dimorphisms ofmRNA and miRNA in human/murine heart disease. PLOS ONE, Vol. 12, e0177988,2017.
• T. Matsuda and W. E. Strawderman. Pitman closeness properties of Bayes shrinkage
procedures in estimation and prediction. Statistics & Probability Letters, Vol. 119, pp.
21–29, 2016.
• T. Matsuda and W. E. Strawderman. Pitman closeness properties of point estimatorsand predictive densities with parametric constraints. Statistics & Probability Letters,Vol. 116, pp. 101–106, 2016.
• T. Matsuda and F. Komaki. Singular value shrinkage priors for Bayesian prediction.
Biometrika, Vol. 102, pp. 843–854, 2015.
• T. Matsuda and T. Matsuo. A new geometric integration approach based on local invariants. JSIAM Letters, Vol. 5, pp. 37–40, 2013.

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