Summary
Laure Berti-Equille is a research director with over a decade focused on designing scalable ML and reinforcement-learning methods for automated data curation, anomaly detection, and multimodal data integration across domains like environmental science, healthcare, and smart cities. As co-founder of the international IDEAL lab at IRD, she bridges academic rigor and applied impact, translating probabilistic modeling and causal inference into tools for sustainability and UN SDG challenges. Her track record includes tenured and visiting positions at institutions such as Aix-Marseille University, MIT, and AT&T Labs, reflecting deep experience in both foundational research and collaborative deployments. Skilled in feature engineering, multivariate test analysis, and hands-on ML engineering (Python, R), she routinely tackles heterogeneous, multi-source datasets often overlooked by mainstream pipelines. Beyond publications and academic leadership, she brings a practical “data core with AI mind” perspective—crafting RL-driven strategies to make messy real-world data reliably usable. Based in France, she combines methodological breadth with a history of building interdisciplinary research infrastructures that connect earth observation, AI, and policy-relevant science.
10 years of coding experience
15 years of employment as a software developer
Habilitation à Diriger des Recherches (HDR), Habilitation à Diriger des Recherches (HDR) at Université de Rennes I
Udacity Nanodegree Program Graduate, Udacity Nanodegree Program Graduate at Udacity
Master's degree in Computer Science, Master's degree in Computer Science at Université Paris Dauphine
Doctor of Philosophy (PhD), Doctor of Philosophy (PhD) at Université de Toulon
French, English