Accelerating Astrophysics Discovery at Yale with AutoML

AutoML for astrophysics discoveries at Yale - NGC 6240 captured by the Hubble Telescope

Yale astronomy researcher Aritra Ghosh spent more than three months manually building a deep learning classifier for galaxy images. When experimenting with Modulos AutoML, he was able to automatically build a similar performance deep learning classifier with two weeks of computational time.

Why selecting and training AI models is a job for machines

AI model selection and training is job for machines - graphic

In the traditional data science process, a team of data scientists and machine learning engineers is required. They will spend days, weeks, or even months on iteratively choosing AI models, tuning them, and testing them. Often, this tedious process is guided by the team’s experience with models and techniques and is colored by their experience and education.

Bringing AI to the right level of abstraction

A team of experts vs. using Modulos - illustration

The idea which led to the foundation of Modulos was a collaboration between the co-founders, Ce Zhang, a computer science professor, and Kevin Schawinski, then an astrophysicist. Both of us were doing research at ETH Zurich and we were trying to see how we could use AI to better analyze and understand astrophysics data.