Ann Zabludoff's research is broadly distributed across extragalactic astronomy and cosmology. She has explored the first generation of stars and galaxies, galaxy evolution via mergers, gravitational lensing, dark matter, supernovae, the intergalactic medium, galactic nuclear activity, galactic spectral classification, the baryon budget of the Universe, stellar disruption by supermassive black holes, exoplanet detection, and the development of large-scale structure. Her work involves analyses of large observational databases and theoretical cosmological simulations, as well as adaptations of astronomical instruments for new science. Her current projects include: 1) using machine learning to classify and study astrophysical transients, 2) collaborating with non-professional astronomers to quickly identify the electromagnetic counterparts of gravitational wave sources, 3) exploring the formation of galaxies and large-scale structure via the simulation and detection of Lyman-alpha emitting sources in the early Universe, 4) counting stellar and gaseous baryons across environments to constrain the baryon budget and to measure changes in the efficiency of star formation, and 5) clocking the transformation of spirals into early-type galaxies via the post-starburst, post-merger phase of galaxy evolution. She is a member of UA's Data7 Data Science Institute, with interests in machine learning, image analysis, and large scale visualization.