Mame Diarra Toure

I am a PhD student at McGill University, where I am being advised by Adam Oberman. In my research, I work on the mathematical model behind reinforcement learning and deep reinforcement learning. I aim to apply those methods to industry problems particularly quantitative finance problems such as asset pricing in high dimension or portfolio and risk management. My work is funded by a Dean fellowship and MILA fellowship.

Prior to joining McGill I worked as a quantitative analyst at Société Général. I did my undergraduate and part of my graduate training in France where I attended Lycée Sainte-Croix Sainte-Euverte and ENSIIE-France's Grandes Ecoles system. I hold an engineering degree in applied mathematics and computer science from ENSIIE and spent the third year of ENSIIE's curriculum at Paris Saclay University where I earned a Master in quantitative finance with first class honors.

Email  /  CV  /  LinkedIn  /  Twitter  /  Github

profile photo

I'm interested in machine learning, deep learning, reinforcement learning, stochastic control problems, stochastic differential equations and partial differential equations. I am currently working on finding a suitable mathematical model that could be use for deep reinforcement learning in rder to solve problem for which the state space is continuous or infinite,

Previous Work and School projects

During my undergraduate and graduate studies degree I had the chance to work on some research projects alone or with my classmates

> Combining Neural Networks Algorithms and Model Diffusion for CVA Pricing
Mame Diarra Toure , Ghada Ben Said, Gabriel Moran, Ouassim Sebbar,Houssem Fendi,Issame Sarroukhe

Using deep neural networks to compute CVA

Option pricing using artificial neural networks
Mame Diarra Toure , Imane Alla

ANN for option pricing

Rough volatility modelling,Kernel Estimation of Volterra processes
Mame Diarra Toure

Estimating the kernel of a rough volatility model

Rough volatility modelling, Lifted heston model
Mame Diarra Toure

Study of lifted heston model