Scholar
rémi genet
Google Scholar ID: XKdeMPcAAAAJ
Université Paris Dauphine
Citations & Impact
Citations
2,444
H-index
4
i10-index
3
Publications
20
Co-authors
2
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- - Developed novel neural network architectures for time series forecasting (e.g., Temporal Kolmogorov-Arnold Networks and Transformers, and Temporal Linear Networks)
- - Integrated path signature methods into neural networks via custom libraries (such as Keras Sig) and hybrid architectures (SigGate, SigKAN)
- - Established a unified differentiable framework that extends from static to dynamic, multi-asset VWAP models
Research Experience
- - Quantitative Researcher at Aplo (formerly SheeldMarket): Conducting research on trading, execution, and risk management. Developed a Python-based service that leverages deep learning models for real-time computation, which feeds into C++ execution systems. Built additional real-time C++ services for derivatives risk monitoring and liquidation algorithms.
- - Lecturer in Computer Science at Université Paris Dauphine-PSL: Teaching and developing course materials for Introduction to Python for Finance (since 2021), Object-Oriented Programming (OOP) in Python (since 2023), API Development in Python (since 2023), Deep-Learning for Finance (since 2025)
Education
- - PhD in Finance, Université Paris Dauphine-PSL (Feb 2023 - expected Jul 2025)
- - Advanced Master in Big Data & Machine Learning, Télécom Paris (2020 - 2021)
- - Master in Economics and Financial Engineering – Specialization in Quantitative Finance, Université Paris-Dauphine PSL (2018 - 2020)
- - Bachelor’s Degree in Applied Economics & Financial Engineering, Université Paris-Dauphine PSL (2017 - 2018)
Background
- - Quantitative Researcher
- - PhD Candidate in Finance (expected completion July 2025)
- - Computer Science Lecturer at Université Paris Dauphine-PSL
- - Research Interests: Deep-Learning Integration in Finance, Time Series Modeling, Open-Source Tools & Reproducible Research
Miscellany
- - Technical Skills: Python (expert), SQL (advanced), Bash (advanced), C++ (intermediate), VBA (intermediate), Java (basics), Rust (basics), R (basics), Matlab (basics), Docker (advanced), GitHub Actions (advanced), poetry & pyenv (advanced), maturin (basics)
- - Languages: French (native), English (fluent)
- - Personal Statement: Driven by the challenge of blending cutting-edge research with practical implementation. Most research is conducted using Keras 3, valuing its backend-agnostic capabilities. Particularly favors the JAX backend for its innovative philosophy and performance benefits.
