shashin

Satoshi Hayakawa

This is Satoshi Hayakawa's research homepage (Japanese).

I am a DPhil student at Mathematical Institute, University of Oxford since October 2020. My research interest is applied probability, including mathematical statistics, numerical analysis and machine learning.

e-mail: hayakawa [at] maths.ox.ac.uk
Google Scholar: link

News

2021.07
A new preprint "Positively weighted kernel quadrature via subsampling" (joint work with Harald Oberhauser and Terry Lyons) has been released. (arXiv)
2021.03
I received Dean's award from Graduate School of Information Science and Technology, The University of Tokyo.
2021.01
A new preprint "Estimating the probability that a given vector is in the convex hull of a random sample" (joint work with Terry Lyons and Harald Oberhauser) has been released. (arXiv)
2020.12
My paper "Monte Carlo cubature construction" has been published in Japan Journal of Industrial and Applied Mathematics.
2020.10
I have started my DPhil in Mathematics at University of Oxford.
2020.08
A new preprint "Monte Carlo construction of cubature on Wiener space" (joint work with Ken'ichiro Tanaka) has been released. (arXiv)

Publication

Preprints

  1. Satoshi Hayakawa, Harald Oberhauser and Terry Lyons. (2021). Positively weighted kernel quadrature via subsampling arXiv:2107.09597
  2. Satoshi Hayakawa, Terry Lyons and Harald Oberhauser. (2021). Estimating the probability that a given vector is in the convex hull of a random sample arXiv:2101.04250
  3. Satoshi Hayakawa and Ken'ichiro Tanaka. (2020). Monte Carlo construction of cubature on Wiener space, arXiv:2008.08219
  4. Satoshi Hayakawa and Ken'ichiro Tanaka. (2019). Convergence analysis of approximation formulas for analytic functions via duality for potential energy minimization, arXiv:1906.03133

Journal Papers

  1. Satoshi Hayakawa. (2020). Monte Carlo cubature construction, Japan Journal of Industrial and Applied Mathematics, published online. (link)
  2. Satoshi Hayakawa and Ken'ichiro Tanaka. (2020). Error bounds of potential theoretic numerical integration formulas in weighted Hardy spaces, JSIAM Letters, 12, 21-24. (link)
  3. Satoshi Hayakawa and Taiji Suzuki. (2020). On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces, Neural Networks, 123, 343-361. (link)
  4. Hisashi Hayakawa, Yusuke Ebihara, David P Hand, Satoshi Hayakawa, Sandeep Kumar, Shyamoli Mukherjee and B Veenadhari. (2018). Low-latitude Aurorae during the Extreme Space Weather Events in 1859, The Astrophysical Journal, 869(1), 57. (link)
  5. Hisashi Hayakawa, Yusuke Ebihara, José M Vaquero, Kentaro Hattori, Víctor MS Carrasco, María de la Cruz Gallego, Satoshi Hayakawa, Yoshikazu Watanabe, Kiyomi Iwahashi, Harufumi Tamazawa, Akito D Kawamura and Hiroaki Isobe. (2018). A great space weather event in February 1730, Astronomy & Astrophysics, 616, A177. (link)
  6. Hisashi Hayakawa, Yusuke Ebihara, David M. Willis, Kentaro Hattori, Alessandra S. Giunta, Matthew N. Wild, Satoshi Hayakawa, Shin Toriumi, Yasuyuki Mitsuma, Lee T. Macdonald, Kazunari Shibata, and Sam M. Silverman. (2018). The Great Space Weather Event during 1872 February Recorded in East Asia, The Astrophysical Journal, 862(1), 15. (link)

Talks

  1. 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.
  2. 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.
  3. Optimization-based cubature construction and its application to cubature on Wiener space
    Probability Young Seminar Online, Online, September 7-9, 2020.
  4. Monte Carlo cubature construction
    16th JSIAM Spring Meeting, Tokyo, March 3-4, 2020.
  5. Convergence analysis of approximation formulas for analytic functions via duality for potential energy minimization
    82nd Kanazawa Analysis Seminar, Kanazawa, January 15, 2020.
  6. On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces (slide)
    Japanese Joint Statistical Meeting, Shiga, September 8-12, 2019.
  7. Convergence analysis of approximation and numerical integration formulas for analytic functions via duality
    JSIAM Annual Meeting, Tokyo, September 3-5, 2019.

Education

2020.10-present
Doctor of Philosophy in Mathematics, the University of Oxford.

Supervisor: Professor Terry Lyons & Associate Professor Harald Oberhauser

2019.04-2020.09
Master of Information Science and Technology, The University of Tokyo.

Supervisor: Associate Professor Ken'ichiro Tanaka

2015.04-2019.03
Bachelor of Engineering, The University of Tokyo.

Supervisor: Associate Professor Taiji Suzuki

Awards and Scholarships

Activities

Others