Abstract　 　　
   Recent times have seen, and will continue to see, the advent of new generations of deep and wide surveys in optical and near-infrared, which enable us to explore the evolution of galaxies on a range of redshifts and masses never explored before. Owing to my involvement in some of these most important surveys, I am exploring the mass assembly history of galaxies from early time (up to z=7).

   Using clustering analysis to probe the halo masses of galaxies, I am probing directly the evolution of luminous-to-dark mass ratio with redshift. In the course of this presentation, I will demonstrate that a halo downsizing is in place since z=2, where galaxy mass assembly happens faster in higher mass haloes at high redshifts, while at lower redshifts, it migrates to lower mass halos. I will establish that the budget of mass is not compatible with the baryonic fraction derived from cosmological studies (CMB).

   Also I will demonstrate that in terms of baryonic mass assembly since z=2, while major mergers have played a minor role in mass assembly of the most galaxies, it may have played an increasingly important role in the lower mass galaxies, particularly at low redshifts; in contradiction with theoretical predictions.

   Finally, I will conclude by highlighting the challenges emerging from the exploitation of current and next generation large surveys, and how they require the development of new fields such as Astroinformatics and Virtual Observatory."

   A brief bio of the speaker: Dr. Sebastien Foucaud graduated from the Laboratoire d'Astrophysique de Marseille in 2003, followed by a Marie Curie postdoctoral fellowship in Milan, Italy; Research Fellowship in Nottingham, UK; and Assistant-Professorship in Taiwan. He joined Shanghai JiaoTong University in September 2013 as Distinguished Researcher. Dr. Foucaud specialises in the study of galaxy evolution and has been a key player in the most important extragalactic surveys in the past decade. His expertise includes developing data reduction and analysis pipeline, and exploiting large datasets through data mining techniques."