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We cannot yet explain the diverse properties of galaxies across cosmic time. Gas accretion, multiscale environments, galaxy merging, and supermassive black hole feedback are potentially important drivers of galaxy evolution. I analyze and interpret multivariate data of galaxies in large surveys using statistical methods to distinguish the main drivers of galaxy evolution and test theoretical models. Thus far my research has focused on tracking the evolution of nearby galaxies that have enhanced star formation rates (SFRs) than average and quenched rapidly. We found that the recent evolution of galaxies is regulated by gas content and its consumption by star formation. Black hole feedback does not have instantaneous effects on gas content or SFR. Moreover, we found that SFR is correlated with morphological asymmetry, central mass concentration, and small-scale environments. Star-forming galaxies (SFGs) in low-density environments, with higher asymmetry, and more prominent bulges have higher SFRs at a given stellar mass (M*). Both mergers and diffuse gas accretion can induce asymmetries, drive gas to the center, elevate SFR, and build a central concentration of stars. Interestingly, local starbursts and recently quenched galaxies live in low-density environments. The starbursts are not mainly triggered by mergers and some of them do not quench rapidly. Obviously, the progenitors of the majority of quiescent galaxies today cannot be found among nearby galaxies. We need future surveys with unprecedented sensitivity to distant galaxies to identify them. I will develop a data-driven framework to track evolution of galaxies across cosmic times using their M* , mass growth histories, structures, environments, and number counts as multiple constraints. In preparation for future surveys (CSST, MSE, SKA, etc.), I will first analyze existing data of nearby galaxies with deep imaging and IFU spectroscopy, galaxies in JWST large programs, the DESI bright galaxy sample, and simulated galaxies in IllustrisTNG using novel methods such as statistical texture analysis and Bayesian spatial and time series models, thereby significantly advance our understanding of how and why multivariate, spatially resolved properties of galaxies evolve with cosmic time.
Dr. Hassen Yesuf is currently a joint Kavli IPMU-KIAA post-doctoral fellow. He did his PhD in astrophysics with emphasis in statistics at University of California Santa Cruz and his undergraduate at Princeton University. His research focuses on multivariate analyses of large galaxy survey datasets using modern statistical methods. His research interests are studying factors that affect galaxy evolution in general and star formation and gas in galaxies in particular. He has experience working on SDSS and Subaru HSC data. He plans to work on big datasets from future surveys such as CSST, DESI, and SKA.