Baharan Mirzasoleiman, assistant professor of computer science at UCLA’s Samueli School of Engineering, received $550,000 from the National Science Foundation and the Simons Foundation to train machine learning algorithms to process astronomical data.
Precise data analysis powered by machine learning could significantly speed up time-consuming processes such as modeling complex chemical reactions inside stars. By spending less time processing data to generate sophisticated models, researchers can devote more effort to discovering new knowledge about the cosmos.
“Currently, the volume of astronomical data is so large that it makes data processing prohibitive,” said Mirzasoleiman, who leads the study. GrosML research group at UCLA Samueli. “My role will be to design algorithms that can efficiently train basic machine learning models by extracting information from huge amounts of astronomical data.”
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 this five-year initiative, which will include an interdisciplinary team of researchers from multiple universities.
