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Astronomy and Cosmology
Computational Tools for Data Science, Data Visualization, Machine Learning, Statistical Modeling
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Oleg Gnedin



I am a theoretical astrophysicist studying the origins and structure of galaxies in the universe. My research focuses on developing more realistic gasdynamics simulations, starting with the initial conditions that are well constrained by observations, and advancing them in time with high spatial resolution using adaptive mesh refinement. I use machine-learning techniques to compare simulation predictions with observational data. Such comparison leads to insights about the underlying physics that governs the formation of stars and galaxies. I have developed a Computational Astrophysics course that teaches practical application of modern techniques for big-data analysis and model fitting.

Emergence of galaxies and star clusters in cosmological gasdynamics simulations. Left panel shows large-scale cosmic structure (density of dark matter particles), which formed by gravitational instability. In the middle panel we can resolve this structure into disk galaxies with complex morphology (density of molecular/red and atomic/blue gas). These galaxies should create massive star clusters, such as shown in the right panel (real image — to be reproduced by our future simulations!).