Many problems in the physical sciences share common statistical challenges including heterogeneous data from multiple probes, uncertainty quantification, ill-posed inverse problems, spatio-temporal data and complex simulations.
In 2018, a group of faculty and students at Carnegie Mellon University (CMU) started the STAMPS research group to develop new statistical and machine learning methodology tailored to the unique challenges that arise across multiple areas in the physical sciences.
STAMPS provides foundational methodology in statistics, data science, machine learning and artificial intelligence for two distinct branches of physical science: (i) Astronomy and Particle Physics, and (ii) Climate and Environmental Science, which include applications in e.g. Oceanography, Meteorology, and Remote Sensing.
In Fall 2024, STAMPS is transitioning from a research group to a STAMPS@CMU Research Center. Our launch event is scheduled for September 20, 2024. Please hold the date!