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Dr. Nader Karimi, Ph.D.

Research Assistant

Present

Amir Kabir University of Technology (Tehran Polytechnic)

Tehran

Islamic Republic of Iran

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I am a Research Assistant at the Institute for Computer Science, Amirkabir University of Technology (Kharazmi Building, Room 218), where my work sits at the intersection of applied mathematics, quantitative finance, and data-driven modeling. My background spans a B.Sc. in Mathematics, an M.Sc. in Financial Mathematics, and a Ph.D. in Applied Mathematics. My research covers option pricing with jumps and memory, machine-learning-assisted decision-making in commodity markets, and mesh-free numerical schemes for PDE/PIDE models. Representative publications include articles in Mathematical Methods in the Applied Sciences (2021), Engineering Analysis with Boundary Elements (2020), Statistics & Probability Letters (2024), and a 2025 paper in Computational Methods for Differential Equations.

  • Stochastic modeling and option pricing (jumps, memory, fractional and mixed-fractional models).
  • Risk management and commodity market modeling; optimal stopping and storage.
  • Numerical methods for PDE/PIDE (meshfree/RBF approaches) and data-driven methods in finance.
  • Karimi, N., Ahmadian, D., Ballestra, L.V., et al. (2021). An extremely efficient numerical method for pricing options in the Black–Scholes model with jumps. Mathematical Methods in the Applied Sciences, 44(2), 1843–1862.
  • Karimi, N., Kazem, S., Ahmadian, D., et al. (2020). On a generalized Gaussian radial basis function: Analysis and applications. Engineering Analysis with Boundary Elements, 112, 46–57.
  • Karimi, N., Salavati, E., Assa, H., Adibi, H. (2024). A stochastic optimal stopping model for storable commodity prices. Statistics & Probability Letters, 109941.
  • Karimi, N., Shahmoradi, M. (2025). Application of Stochastic Runge–Kutta Methods for Mixed Fractional Brownian Motion Processes. Computational Methods for Differential Equations. (Now published; DOI listed in CV.) 
  • Karimi, N., Assa, H., Salavati, E.et al.Calibration of Storage Model by Multi-Stage Statistical and Machine Learning Methods.Comput Econ62, 1437–1455 (2023). https://doi.org/10.1007/s10614-022-10304-z
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