Summary
Liza Sazonova is a postdoctoral fellow and Rubin LSST morphology pipeline contributor based in Waterloo, combining 11 years of physics and data-science experience to study how spiral galaxies quench and transform into bulge-dominated systems. She applies convolutional neural networks, multi-wavelength observations, and morphology analysis to trace the coordinated evolution of stars, gas, and dust across environments and cosmic time. Her PhD and fellowship work spans collaborations with Zooniverse and SPOGs, reflecting a blend of citizen-science, machine learning, and observational astronomy. Prior roles in industry and teaching honed her software engineering, pattern-recognition, and curriculum development skills, enabling her to translate complex astrophysical problems into reproducible code and pipelines. An offbeat strength: she frames galaxy evolution problems with the curiosity of a detective—she calls it “solving murders of galaxies”—which fuels creative modeling and interpretation.
11 years of coding experience
1 year of employment as a software developer
Johns Hopkins University
Bachelor of Science (B.S.), Mathematical Physics (Astrophysics specialization), Dean's Honour List, Bachelor of Science (B.S.), Mathematical Physics (Astrophysics specialization), Dean's Honour List at University of Waterloo
English, Russian