معرفی
Gersende Fort is a CNRS Senior Researcher affiliated with the Institut de Mathématiques de Toulouse (IMT) at the University of Toulouse. Her research focuses on stochastic approximation methods, Bayesian statistics, optimization algorithms, and computational statistics. She has presented at major conferences such as ICASSP 2025 (Suzhou & Hyderabad) and the French-German-Spanish conference on Optimization (Gijon, 2024), often collaborating with researchers like Eric Moulines and Hoi To Wai.
Recent work includes developing sampling techniques for nonsmooth log-concave densities, hierarchical Bayesian models for epidemiological analysis (e.g., COVID-19 reproduction number estimation), and federated learning algorithms. She leads the MAD project funded by the French National Research Agency (ANR), advancing scalable optimization methods. Her publications span technical reports on stochastic proximal-gradient algorithms, fluid-limit-based MCMC tuning, and PLS classification in microarray data analysis.
Fort actively engages in academic outreach, including the 'AI and Society' Summit in Paris (2025) and a workshop on mathematics of machine learning. She also contributed to educational initiatives like the Women and Mathematics event in Lavelanet (2024). Her research bridges theoretical foundations with applications in health, optimization, and machine learning.
