Carnegie Mellon University
LINCC Frameworks Project Scientist
Alex Malz is a cosmostatistician with interests spanning extragalactic astronomy in the uncertainty-dominated regime, focusing on the use of large quantities of low-information-density photometry to infer the physics of the dark universe. He develops and applies principled mathematical tools to extract physical information from noisy data to address problems ranging from photometric redshift estimation to light curve classification; he is a member of the Rubin PZ Commissioning Team as well as the PLAsTiCC/ELAsTiCC Team. He also conducts research on approaches to optimize design decisions for astronomical surveys and analyses thereof for a variety of static and time-domain science goals in the contexts of the DESC, ISSC, TVS, AGN, and Galaxies Science Collaborations. Alex loves to hack and is passionate about open software development and collaboration-building.