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Satoshi Hayakawa

This is Satoshi Hayakawa's research homepage. Japanese

I am an assistant professor at The University of Tokyo since April 2026. Before that, I was a researcher at Sony Group Corporation from March 2024 to March 2026. I completed my PhD studies as a DPhil student at Mathematical Institute, University of Oxford from October 2020 to January 2024 (thesis). My research interest in PhD is applied probability, including mathematical statistics, numerical analysis and machine learning; random convex hulls and kernel quadrature in particular. I am currently interested in probabilistic generative models and more generally probability distributions, especially in discrete and/or high-dimensional settings.

e-mail: lastname [at] mist.i.u-tokyo.ac.jp
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    English

    1. Nyström approximation and convex kernel quadrature, ICIAM 2023, Tokyo, August 24, 2023.
    2. Positively weighted kernel quadrature and a refined analysis of Nyström approximation, IFAM Seminar, Liverpool, March 22, 2023.
    3. Positively weighted kernel quadrature and a refined analysis of Nyström approximation, Probabilistic methods, Signatures, Cubature and Geometry, York, January 9, 2023.
    4. Hypercontractivity meets random convex hulls: analysis of randomized multivariate cubatures, Berlin SPDE Seminar, Berlin, November 10, 2022.
    5. Random convex hull, cubature, hypercontractivity, 50th Saint-Flour Probability Summer School, Saint-Flour, July 11-23, 2022.
    6. Random convex hull, cubature, hypercontractivity, York University Stochastic Analysis Seminar, York, June 13, 2022.
    7. Random convex hull, cubature, hypercontractivity, 15th Berlin-Oxford Young Researchers Meeting on Applied Stochastic Analysis, Berlin, May 12-14, 2022.
    8. Random convex hull, cubature, hypercontractivity, Toulouse Probability Seminar, Toulouse, May 10, 2022.
    9. Random convex hull, cubature, hypercontractivity, QIP-QML 2022, Vienna, March 10, 2022.
    10. Random convex hull, cubature, hypercontractivity, Rough Path Interest Group at DataSig, Online, January 26, 2022.
    11. Estimating the probability that a given vector is in the convex hull of a random sample, 14th Oxford-Berlin Young Researchers Meeting on Applied Stochastic Analysis, Online, February 10-12, 2021.

    Japanese

    1. Distillation of discrete diffusion through dimensional correlations, Numerical Analysis Workshop at RIMS, Kyoto, October 8-10. 2025.
    2. Random convex hulls and kernel quadrature, UTokyo Numerical Analysis Seminar, Tokyo, May 29, 2024.
    3. Approximating expectation with random convex hulls and Bayesian quadrature, Japanese Joint Statistical Meeting, Kyoto, September 3-7, 2023.
    4. Approximating expectation with random convex hulls and Bayesian quadrature, Probability Young Summer Seminar, Kyoto, August 28-31, 2023.
    5. Optimization-free construction of kernel quadrature, JSIAM Annual Meeting, Spporo, September 8-10, 2022.
    6. Recombination, random convex hull, cubature, Tokyo Probability Seminar, Online, April 18, 2022.
    7. Random convex hull, cubature, hypercontractivity, Probability Early-Spring Seminar, Online, March 3, 2022.
    8. Random convex hulls and kernel quadrature, Probability Young Seminar Online, August 23, 2021.
    9. Point processes and subsampling for numerical integration, Intersections of complex networks in real world and infinite particle systems II, Online, August 19, 2021.
    10. Estimating the probability that a given vector is in the convex hull of a random sample, Probability Seminar at Kansai University, Online, April 24, 2021.
    11. Optimization-based cubature construction and its application to cubature on Wiener space, Probability Young Seminar Online, September 7-9, 2020.
    12. Monte Carlo cubature construction, 16th JSIAM Spring Meeting, Tokyo, March 3-4, 2020.
    13. Convergence analysis of approximation formulas for analytic functions via duality for potential energy minimization, 82nd Kanazawa Analysis Seminar, Kanazawa, January 15, 2020.
    14. On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces, Japanese Joint Statistical Meeting, Shiga, September 8-12, 2019. slides
    15. Convergence analysis of approximation and numerical integration formulas for analytic functions via duality, JSIAM Annual Meeting, Tokyo, September 3-5, 2019.

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