👩🏼🏫 Awards and Certificates
Embed rich media such as videos and LaTeX math🏆 Award: 2025 Best European Master’s Thesis in Mathematical Finance.
Recipient of the 2025 Natixis Award for Best Master’s Thesis in Mathematical Finance, awarded by the Natixis Foundation for Research and Innovation. | PDF of my Master’s thesis |
Related Work
Below is the research paper associated with this award:
Video
Here I share the Full ceremony replay: | Images of the ceremony |
Dailymotion:
Video file
Videos Videos of some annimation:
| Training Skew | Volatility Fitting |
|---|---|
Podcast
{{< audio src="ambient-piano.mp3" >}}
Try it out:
Math block:
$$ \textcolor{red}{\text{Action network:}} \quad \text{DPG} = \textcolor{blue}{\text{Deep Policy Gradient}} $$$$ a_t \sim \textcolor{red}{\pi^{D}(s_t,\theta^{\pi})} + \epsilon_t, \quad \text{with} \quad \textcolor{brown}{\epsilon_t \sim \mathcal{N}(0, \sigma_n^2 I_K)}, \quad \text{and} \quad \sigma_n = \max\!\left(\sigma_0\left(1-\frac{n}{N}\right)^4,\sigma_{\min}\right) $$$$ \textcolor{red}{\text{Critic network:}} \quad \textcolor{blue}{\text{Q-Learning and Bellman equation}} $$$$ \begin{cases} R_{t}=\sum_{i=t}^{T}\gamma^{(i-t)} r(s_{i},a_{i}) \\[6pt] Q^{\pi}(s_{t},a_{t})=\mathbb{E}[R_{t}\mid s_{t},a_{t}] \end{cases} \quad \Rightarrow \quad \textcolor{red}{ L(\theta^{Q})= \mathbb{E}\left[ \left( Q^{\pi}(s_{t},a_{t}; \theta^{Q})- \left(r(s_{t},a_{t})+\gamma Q^{\pi}(s_{t+1},a_{t+1};\theta^{Q})\right) \right)^{2} \right] } $$Latex code
\begin{itemize}
\item \textcolor{red}{Action network:} DPG= \textcolor{blue}{Deep Policy gradient} % $ J = \mathbb{E}[R_{s_{t}}] ==== $ (\textbf{Proof:} Cf. Report)
\end{itemize}
$$
a_t \sim \textcolor{red}{\pi^{D} (s_t,\theta^{\pi})} + \epsilon_t \quad \text{with} \quad \textcolor{brown}{\epsilon_t \sim \mathcal{N}(0, \sigma_n^2 I_K)} \quad \text{and} \quad \sigma_n = \text{max}(\sigma_0(1-\frac{n}{N} )^{4},\sigma_{\text{min}})
$$
\begin{itemize}
\item \textcolor{red}{Critic network:} \textcolor{blue}{Q-Learning and Bellman equation.} % $ Q_{\theta^{Q}}(s_{t_{i}},a_{t_{i}}) = - \sum_{k=t_i}^{T} \mathbb{E}_{(s_{k},a_{k})\sim \rho_\pi} [ \gamma^{(k-t_i)} \xi (\vec{\theta}_{t_{k}} )] $
\end{itemize}
$$
\begin{cases} R_{t}=\sum_{i=t}^{T}\gamma^{(i- t)}r(s_{i},a_{i})\\Q^{\pi}(s_{t},a_{t})=\mathbb{E}[R_{t}|s_{t},a_{t}]&
\end{cases} \quad \Rightarrow \textcolor{red}{L(\theta^{Q})=\mathbb{E}[(Q^{\pi}(s_{t},a_{t}; \theta^{Q})-[r(s_{t},a_{t})+\gamma Q^{\pi}(s_{t+1},a_{t+1};\theta^{Q})])^{2}]}
$$
Hi, welcome to my website! I am a Research Scientist in Mathematics working on stochastic analysis, optimal control, diffusion models, and statistics, with applications to mathematical finance and machine learning.
Prior to this, I studied at École Polytechnique, where I earned an engineering degree with a major in mathematics. I also obtained a Master’s degree in Probability and Finance from IP Paris, jointly with Sorbonne Université, graduating with highest honors (mention Très Bien) and received a bachelor’s degree in Philosophy from Université Paris Nanterre.