Dr. Idrissa Niakh - Researcher Profile

About Me

Always looking for knowledge and challenges.

With a solid background in applied mathematics and computer science, my research activities focus on quantitative finance, computational finance, and machine learning.

Experience

Young Researcher, Command Strategy Advisory

Jan 2023 - Present | Paris, France

Research & Development in Basel III (FRTB-CVA), CVA risk, and SA-CVA tool development.
Developed pricing libraries in C++ and Python for fixed-income and derivative securities.
Coordination of internal research projects and in-house training courses.
Creation of Research Tax Credit (CIR) files.
Regulatory, scientific, and technology monitoring; contributor to the CSA Newsletter.

PhD Engineer, EDF

Nov 2019 - Nov 2022 | Palaiseau, France

Developed reduced basis methods for variational inequalities in Code_Aster.
Established theoretical stabilization results for model reduction techniques.
Applied model reduction to mechanical contact problems.
Supervised an intern on Nitsche’s method for contact problems.

Teaching Experience

École des Ponts ParisTech: Reduced Basis Method (2021), Numerical Analysis (2020).
Université Paris-Dauphine: Numerical Methods & Optimization (2021).
Sorbonne Université: Numerical Analysis (2020).

Education

École des Ponts ParisTech

Doctor of Philosophy (PhD) in Applied Mathematics (2019 - 2022)

Thesis Supervision: Alexandre ERN, Virginie EHRLACHER | Advisor: Guillaume DROUET

University of Strasbourg

MSc in Scientific Computing and Mathematics of Innovation (2017 - 2019)

Grade: With Highest Honour | Excellence Scholarship: LABEX IRMIA

Paris-Sud University (Paris XI)

BSc (Hons) in Fundamental Mathematics (2016 - 2017)

Université Gaston Berger

BSc in Mathematics, Physics and Informatics (MPI) (2012 - 2015)

Grade: With Highest Honour | Delegate of the Faculty of Applied Sciences and Technologies

Projects

Basel III - Credit Valuation Adjustment (FRTB-CVA)

Study of Basel III regulatory framework on CVA risk.
Development of an SA-CVA calculation tool.

Development of a Pricing Library

Implemented in C++ and Python.
Supports fixed-income securities and yield curve modeling.
Includes derivatives (FRA, IRS, Options).

FUI Mordicus (2018-2019)

Specification of a data standard for non-intrusive model reduction.
Implementation in C++ and Python.

Study of Datasets in Python (2018)

Observation and description of data.
Preprocessing pipeline setup.
Creation of classification models and performance metrics.

Connected Mobility / Pedestrian Navigation Assistance by GPS (2018)

Real-time retrieval of smartphone GPS data.
Developed tools in Python for detecting and simulating signal disturbances.
Developed correction mechanisms for signal errors.

Linear Solvers (2015)

Theoretical research on linear solvers.
Developed a MATLAB library for direct and iterative solvers.

Hybrid Cryptography / Key Encapsulation Mechanism (2015)

Research on hybrid cryptography methods.
Implemented a Key Encapsulation Mechanism (KEM) in C.

Publications

A reduced basis method for frictional contact problems formulated with Nitsche's method
SMAI-JCM
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Stable model reduction for linear variational inequalities with parameter-dependent constraints
ESAIM: M2AN
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Idrissa NIAKH, PhD