Breakthrough in Astronomy: NSF Funds $550,000 Project to Develop Machine Learning for Astronomical Data Analysis

Los Angeles, CA - A significant grant has been awarded to Baharan Mirzasoleiman, an assistant professor of computer science at the University of California, Los Angeles (UCLA) Samueli School of Engineering, by the National Science Foundation (NSF) and The Simons Foundation. Mirzasoleiman will receive $550,000 to work on a five-year project aimed at training machine-learning algorithms to process enormous amounts of astronomical data.

The objective of this project is to accelerate complex data analysis in astronomy by leveraging machine learning capabilities. With accurate data processing, researchers can generate sophisticated models and uncover new insights about the cosmos more efficiently.

"The volume of astronomy data is so large that it makes data processing prohibitively expensive," Mirzasoleiman stated. "My role will be to design algorithms that can efficiently train foundation machine learning models by extracting information from massive amounts of astronomy data."

This funding is part of a larger five-year grant established through the NSF-Simons AI Institute for Cosmic Origins. Researchers at universities across several institutions will form a cross-disciplinary team, led by those at the University of Texas at Austin.

The breakthrough in this project has significant implications for future astronomical discoveries and will benefit researchers to come.

Read more about Baharan Mirzasoleiman's research project on the UCLA Samueli website.