Speaker
Dr
Michael Kagan
Description
High Energy Physics probes the mysteries of the universe using some of the worlds largest experiments and datasets. To interpret and analyze this data in search of new physical phenomena, a wide array of domain specific algorithms have been developed. At the same time, recent advances in deep learning have seen great success in the realms of computer vision, natural language processing, and broadly in data science. By connecting the challenges in the HEP domain with those in deep learning, new and powerful approaches to analyzing HEP data are being developed. In this talk, I will discuss developments in the application of machine learning techniques to the analysis and interpretation of High Energy Physics data, with a focus on the Large Hadron Collider.
