معرفی
Sarah Lemler is a Researcher at the Laboratory Mathematics and Computer Science for Complexity and Systems, specializing in high-dimensional statistics and non-parametric estimation methods. Her work bridges mathematical statistics with biomedical applications in oncology and neuroscience.
- Primary research areas: Survival Analysis, Hawkes Processes, Diffusion Models
- Key applications: Genomics, Childhood Cancer Survivor Studies, Cardiac Disease Prediction
Her recent publications (2024-2025) focus on adaptive estimation in Cox models, variable selection in high-dimensional mixed-effects models, and neural network applications to multivariate Hawkes processes in neuroscience. She contributes to methodological developments for analyzing counting processes and stochastic differential equations with jumps.
Collaborations span the French Childhood Cancer Survivor Study (FCCSS) and interdisciplinary projects combining dosiomics with machine learning to predict radiation-induced valvulopathy. Her 2025 thesis "Estimation for counting processes with high-dimensional covariates" represents a culmination of her methodological innovations.
