CV

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Contact Information

Name Kentaro Hoshisashi
Professional Title Computer Scientist
Email k.hoshisashi@ucl.ac.uk
Location London,

Professional Summary

Quantitative researcher in scientific machine learning and mathematical finance. Physics-informed neural networks (DC-PINNs) for financial PDEs, stochastic local volatility calibration, and arbitrage-free implied volatility surface calibration, with high-performance JAX implementation.

Experience

  • 2022 - Present

    London, United Kingdom

    Quantitative Researcher, EMEA Quantitative Research
    SMBC Bank International plc
    Exotic derivatives library development; FX exotics pricing and hedging; numerical stabilisation and reproducible quantitative tooling.
  • 2025 - Present

    London, United Kingdom

    Honorary Research Fellow
    University College London, Department of Computer Science
    Research on physics-informed neural networks for financial PDEs, stochastic local volatility, and implied volatility surface calibration.
  • 2018 - 2022

    Tokyo, Japan

    Quantitative Research
    Sumitomo Mitsui Banking Corporation
  • 2016 - 2018

    New York, United States

    Quantitative Research
    SMBC Capital Markets, Inc.
  • 2013 - 2016

    Tokyo, Japan

    Quantitative Research
    Sumitomo Mitsui Banking Corporation

Education

  • 2022 - 2025

    London, United Kingdom

    PhD
    University College London
    Computer Science
    • Faculty of Engineering Sciences, Department of Computer Science.
    • Thesis on physics-informed neural networks for derivative-constrained PDEs in finance.
  • 2010 - 2013

    Tokyo, Japan

    MSc
    The University of Tokyo
    Advanced Materials Science
    • Graduate School of Frontier Sciences, Department of Advanced Materials Science.
  • 2006 - 2010

    Tokyo, Japan

    BSc
    Tokyo Institute of Technology
    Science

Awards

  • 2024
    Best Paper Award, IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)
    IEEE Computational Intelligence Society

    For “Whack-a-mole Learning: Physics-Informed Deep Calibration for Implied Volatility Surface”.

  • 2012
    Student Presentation Award
    Japanese Society for Synchrotron Radiation Research
  • 2011
    Presentation Encouragement Award
    Japan Society of Applied Physics

Publications

  • 2026
    Physics-informed neural networks for solving derivative-constrained partial differential equations
    Physical Review E
  • 2025
    Probability-Density-Consistent Physics-Informed Neural Networks for Stochastic Local Volatility Model Calibration
    Proceedings of the 6th ACM International Conference on AI in Finance (ICAIF)
  • 2024
    Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface
    Proceedings of the 5th ACM International Conference on AI in Finance (ICAIF)

Skills

Scientific machine learning: PINNs, DC-PINNs, inverse problems, Fokker-Planck
Quantitative finance: Exotic derivatives, stochastic local volatility, implied volatility surfaces, FX options
Programming and HPC: JAX, Python, automatic adjoint differentiation (AAD), high-performance computing

Languages

Japanese : Native
English : Fluent (professional)