Welcome Remark (David Parkinson & Inkyu Park)

Applications of Machine Learning Algorithms and their Challenges in Astronomy

• Minsu Shin

Astronomy has a long history of exploiting modern hardware architectures and algorithms in its data analysis research. The most recent trend includes applications of cutting-edge machine learning algorithms. In this talk, I will introduce several application cases of machine learning algorithms, focusing on data science with big survey data, and some challenges and problems in these applications will be presented with some possible ways to tackle these issues.

Gaussian Process Regression in Cosmology

• Ryan Keeley

Gaussian process regression is a data driven statistical tool that is useful for both reconstructions and model selection. Especially in this era of precision cosmology where tensions abound, this example of model-independent statistical methods can be crucial for identifying the new physics or systematics that is the explanation for the tensions. In this talk I will review the formalism of Gaussian process regression. In particular, I will discuss the “dos and donts” of this statistical tool and point out common pitfalls and how to avoid them. Further, I will discuss recent interesting cosmological results found using Gaussian process regression.

Break

Discussion #1 : Summary of previous KAML projects

Discussion #2 : New KAML collaborational projects