Gaussian Process Fitting: let the data guide you!
This short talk will introduce attendees to the concepts of data-driven fits and Gaussian Process (GP), which are used in many different areas in Industry and Academy. It will be briefly explained why and when to use GP with real-life data and also show how to implement this method using Python. This talk is for everyone and requires basic knowledge of statistics and Python.
I am a PhD candidate in Astrophysics at the University of Southampton, UK. I work with Supernovae, which are exploding stars. More specifically, I use “Type Ia (one-A) Supernovae” as distance ladders (i.e., to measure distances) in order to have a better understanding of the Universe at large scales, its expansion rate and what causes this expansion (the so-called “Dark Energy”). In addition, I really enjoy using new approaches/methods in my research by trying different machine and deep learning algorithms. I would consider myself a Python expert, but there is still a lot I need to learn and want to discover.