Mikael Kuuselaمشاهده پروفایل
استادیار
Mikael Kuusela is an Assistant Professor of Statistics and Data Science at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. He specializes in developing statistical methods for physical sciences, focusing on ill-posed inverse problems, spatio-temporal data, and uncertainty quantification in climate science, oceanography, remote sensing, and particle physics. His work integrates closely with domain scientists, including collaborations with oceanographers on Argo floats, NASA's OCO-2 mission, and CERN's CMS experiment. Education: PhD in Statistics, École Polytechnique Fédérale de Lausanne (EPFL), 2016 MSc and BSc in Engineering Physics and Mathematics, Aalto University, 2012 and 2010 His research interests span statistical methodologies for large-scale datasets, with applications to environmental science and high-energy physics. Key areas include: Statistical methods for physical sciences (STAMPS group coordination) Uncertainty quantification in climate models and ocean heat content Optimal transport and inverse problem solutions in particle physics Spatio-temporal modeling of oceanographic phenomena Recent articles highlight advancements in uncertainty quantification, climate model parameter estimation, and applications of statistical techniques to ocean and atmospheric data. Kuusela is also a core member of the US CLIVAR Ocean Uncertainty Quantification Working Group and coordinates the Statistical Oceanography Working Group. His work emphasizes collaboration with domain experts, leveraging statistical rigor to address real-world challenges in environmental and fundamental physics research.
