Machine Learning

In astronomy, the volume and complexity is increasing all the time, which can be challenging for traditional analysis methods. The rapid progress in machine learning and deep learning technqiues offer us an opporunity to approach these problems in different ways. I'm working building the transition layer necessary take advantage of the advances in machine learning and apply them to astronomical problems.

Astrophysics Data Lab

I formed the Astrophysics Data Lab in 2020 to:

  • Answer fundamental questions in astrophysics using the increasingly large datasets that are available now and in the future.
  • Build the framework for translating machine learning methods to astrophysics.
  • Develop innovative ways of using data for astrophysics.
  • Help astronomers integrate new data-driven methods and practices into their work.
Please visit our website for more information about members of the Lab and more details on our work.

Machine Learning Reading Group

Machine learning is a topic that has risen in prominence recently as we get more and more data. We are seeing techniques from machine learning used more widely in astronomy. The goal of this reading group is to become more familiar with topics in machine learning and its connections to statistical tools that are in use in Astronomy. The plan is to go through a couple of textbooks on machine learning and discuss the basic underlying principles and methods. Each week, members of the reading group would present a topic with associated code implementing the algorithm.

The reading group github page is at: https://github.com/UCLAMLRG. Some of the links to books and resources are to the right.

Links/Resources