Astronomy happens to be a field characterized by big data today. In the past couple of decades, the proliferation of ground- and space-based astronomical surveys across various wavelengths has exponentially increased data volumes and complexity. This growth is driven by our quest to address diverse scientific queries, advancements in novel instrumentation, the development of large observational facilities, and improved, affordable data storage capacities. However, this abundance of data also presents significant challenges. Traditional data analysis methods are increasingly inadequate for handling the sheer scale and complexity of contemporary astronomical datasets. Innovative Data Analysis, Machine learning (ML) and Artificial Intelligence (AI) techniques offer a promising solution, providing robust tools for extracting meaningful insights from large and intricate datasets. By leveraging algorithms capable of learning from and adapting to data patterns, ML & AI methods enable astronomers to efficiently process extensive data volumes, identify subtle correlations, and derive physical parameters and insights about astronomical phenomena.

STUDENT MEMBERS:

Aditi, Ariful, Arista, Bhaswati, Bipradip, B. Shriya, Baharul, Dhritimaan, Dibyajyoti, Inzamamul, Lakhi, Nilpawan, Maharnab, Manas, Pallabjyoti, Parvez, Pratyush, Pallabi, Pratyashee, Pranjit, Priyanka N., Partha., Priyanka C., Runa, Rissnalin, Ribanda, Rizwana, Rituraj, Swagata C., Shahida, Swagata, Trishanku, Neelanjana, Mitali, Bhaswati, Anirudha, Dikshita, Suraj, Hirakjyoti, Manazira, Sangeeta, Nabajit.