Machine Learning in Astrophysics: Pioneering the Dawn of a New Era

Scientific Research has recently evolved by performing data-driven modeling via machine learning (ML) techniques. ML is used to make machines "intelligent" by allowing them to learn from data (i.e. experience). Integrating ML in astrophysics can open new frontiers, revolutionize data analysis, and improve our understanding. However, challenges persist in deploying ML in Astrophysics research, including the need for robust algorithms, data limitations, and the interpretability of AI-driven decisions in critical scenarios. As advancements continue, ML and astrophysics exploration synergy promises groundbreaking discoveries and enhanced capabilities for humanity's endeavors beyond Earth's boundaries. This talk will explore the multifaceted potential applications of ML in multiple domains of astrophysics, for example, gravitational wave detection, exoplanet studies, astrochemistry.