معرفی
Romain Pic is a Researcher (Post-doctoral) at the Research Institute for Statistics and Information Science, University of Geneva. He holds a PhD in Statistics and Machine Learning from Université Bourgogne Franche-Comté (2024), and Master’s degrees in Statistics and Machine Learning (Université Lyon 2) and Physics of Complex Systems (Sorbonne Université). His research focuses on probabilistic forecasting, machine learning applications in meteorology, and statistical evaluation methods. He has collaborated with institutions like ETH Zurich and Météo-France.
Key research interests include theoretical properties of probabilistic forecasts, ensemble postprocessing, and the development of proper scoring rules for multivariate predictions. Recent work includes advancements in distributional regression U-Nets for precipitation forecasting and aggregation-based scoring rules. His articles emphasize practical applications in weather systems and methodological contributions to statistical theory.
Publications span topics such as CRPS evaluation frameworks, Wasserstein distance applications, and ensemble forecast verification. Romain has contributed to open-source code repositories for his methodologies, reflecting his commitment to reproducible research.
