Yu Feng and Grigor Aslanyan discuss optimization and Dan Foreman-Mackey discusses sampling methods on Thursday of Astro Hack Week 2016.
Event Description: AstroHackWeek is, in part, a summer school. The mornings offer lectures and exercises covering essential skills for working effectively with large astronomical datasets. Past years have seen topics such as machine learning, Bayesian inference, frequentist statistics, databases, numerical Python, and visualization. AstroHackWeek is also an unconference and hackathon. The afternoon every day is entirely unstructured, and offers opportunities for collaborative research, breakout sessions on special topics, and application of the concepts covered during the morning sessions. Come with a project in mind, join someone else's or apply a new skill to an old problem.
My study in cosmology focuses on the formation of galaxies in the large-scale structure of the Universe. Cosmology is a data-driven science. I develop the necessary software and tools that can efficiently handle these data on platforms from laptops to supercomputers, including (1) massively parallel software to solve gravity and hydrodynamics on tens of thousands of computing nodes, (2) tools to visualize density estimation of large particle datasets with hundreds of billions of particles, and (3) algorithms to understand the clustering of galaxies. I also contribute to open source data science software projects as a user developer. I strongly believe in the power of adequate tools in data-driven science research.