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Rémi Genet is a researcher and lecturer in machine learning and computer science at Université Paris-Dauphine. He is currently pursuing his PhD in Finance, sponsored by Aplo, where his research focuses on deep learning techniques for forecasting and optimal trading execution in cryptocurrency markets. His academic work combines expertise in computer science, quantitative finance, and digital asset markets.
Genet R., Riva F. (2025), Poster Session: Deep Learning for VWAP Execution, 16th Annual Hedge Fund Research Conference, Paris, France
Genet R., Inzirillo H. (2024), Poster Session: Kolmogorov-Arnold Networks For Time Series Forecasting, 3e édition des Dauphine Digital Days, Paris, France
Genet R., Inzirillo H. (2025), Keras Sig: Efficient Path Signature Computation on GPU in Keras 3, ArXiv, 17 p. 
Inzirillo H., Genet R. (2025), STAN: Smooth Transition Autoregressive Networks, ArXiv
Genet R., Inzirillo H. (2024), CaAdam: Improving Adam optimizer using connection aware methods, ArXiv, 11 p.
Genet R., Inzirillo H. (2024), A Temporal Linear Network for Time Series Forecasting, ArXiv, 37 p.
Genet R., Inzirillo H. (2024), A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting, ArXiv, 8 p.
Inzirillo H., Genet R. (2024), SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series, ArXiv, 8 p.
Inzirillo H., Genet R. (2024), A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts, ArXiv, 14 p.
Genet R., Inzirillo H. (2024), TKAN: Temporal Kolmogorov-Arnold Networks, ArXiv, 6 p.
