Mame Diarra Toure

I am a PhD candidate in Applied Mathematics at McGill University, advised by David Stephens. My research focuses on Bayesian Deep Learning and Uncertainty Quantification.

I develop scalable variational methods to make neural networks robust, interpretable, and computationally efficient. My goal is to bridge the gap between theoretical rigor and practical deployment to build safe and trustworthy AI systems.

In 2024, I was awarded the Women in AI Excellence Scholarship from Mila (Quebec AI Institute).

Prior to McGill, I worked as a Quantitative Analyst at Société Générale. My academic foundation was built in France, where I completed the intensive Classes Préparatoires aux Grandes Écoles before earning an Engineering Degree from ENSIIE and an MSc in Quantitative Finance from Paris-Saclay University, where I graduated with First Class Honours and was awarded the Sophie Germain Excellence Scholarship by the Jacques Hadamard Mathematic Foundation(FMJH)

Email / CV / GitHub

Research & Projects

My primary research focuses on developing scalable low-rank variational inference methods (W ≈ AB^T) for Bayesian neural networks. I also investigate whether these structures allow networks to automatically adapt their complexity to the data (Occam's Razor).

PhD Research	Scalable Low-Rank Bayesian Neural Networks Mame Diarra Toure Developing a variational inference framework that factorizes weight matrices to reduce parameter complexity from O(n²) to O(n), enabling uncertainty quantification in large-scale models.
Natixis / Master's Project	Combining Neural Networks and Model Diffusion for CVA Pricing Mame Diarra Toure, Ghada Ben Said, Gabriel Moran, Ouassim Sebbar, Houssem Fendi, Issame Sarroukhe Applied Deep Neural Networks to solve high-dimensional PDEs for Credit Valuation Adjustment (CVA), overcoming the curse of dimensionality in traditional finite difference methods.
Academic Project	Option Pricing Using Artificial Neural Networks Mame Diarra Toure, Imane Alla Building ANN models to learn option price surfaces from market data.
LaMME Research Internship	Rough Volatility Modelling: Kernel Estimation Mame Diarra Toure Implemented statistical techniques to estimate the kernel of Volterra processes, validating the Rough Volatility hypothesis in financial time series.

Experience

McGill University Fall 2025, Winter 2026	Graduate Teaching Assistant TA for MATH 139: Calculus with Precalculus. Leading problem-solving tutorials and strengthening students' conceptual understanding of limits, differentiation, and functions. TA for MATH 141: Calculus 2. Leading problem-solving tutorials and strengthening students' conceptual understanding of the definite integral, techniques of integration, applications and introduction to sequences and series. .
JACOBB (Center for Applied AI) 2022–2024	Research Scientist Contributed to applied R&D projects in NLP and Reinforcement Learning. Designed a collaborative filtering matchmaking algorithm for startups and optimized budget pacing for advertising using RL.
J.P. Morgan Summer 2023	Quantitative Research Mentorship Participant Selected for an exclusive mentorship program providing in-depth exposure to quantitative research methodologies, financial modeling, and career pathways in global markets.
Société Générale 2021–2022	Quantitative Analyst Contributed to the Haussmann Project. Developed and calibrated PD (Probability of Default) and LGD (Loss Given Default) models for low-default portfolios.
BNP Paribas 2021	Quantitative Research Intern Developed mathematical models to quantify the bank's exposure to Central Counterparty Clearing Houses (CCPs). Modeled default fund contributions using Monte Carlo simulations.

Community & Mentorship

I am an active member of Women in AI North America and the Association of Women in Mathematics. I also serve as a mentor at Jiggen In STEM, supporting high school girls in Senegal, and was the former representative of AFSA Québec at McGill.