Baharan Mirzasoleiman, an assistant professor of computer science at the UCLA Samueli School of Engineering, has received $550,000 from the National Science Foundation and the Simons Foundation to train machine-learning algorithms to process astronomical data.
Accurate data analysis powered by machine learning could greatly accelerate time-consuming processes such as modeling the complex chemical reactions inside stars. With less time sunk into processing data to generate sophisticated models, researchers can devote more effort to uncovering new insights about the cosmos.
“Currently, the volume of the astronomy data is so large that it makes data processing prohibitively expensive,” said Mirzasoleiman, who leads the BigML research group at UCLA Samueli. “My role will be to design algorithms that can efficiently train foundation machine learning models by extracting the information from massive amounts of astronomy data.”
The funding for Mirzasoleiman’s project is part of a larger grant that established the NSF-Simons AI Institute for Cosmic Origins. Researchers from the University of Texas at Austin will lead the five-year initiative, which will include a cross-disciplinary team of researchers from several universities.
