The Journal of the Indian Society for Probability and Statistics is dedicated to advancing research in probability, statistics, and their applications across various fields of science and technology. The journal provides a platform for original theoretical and applied contributions that deepen understanding of probabilistic models, statistical methodologies, and data-driven insights relevant to both regional and global contexts. The Journal welcomes high-quality research articles, reviews, and methodological developments in probability theory, statistical inference, stochastic processes, Bayesian methods, multivariate analysis, data science, and computational statistics. It encourages work that addresses contemporary challenges in diverse domains, including but not limited to finance, healthcare, environmental science, machine learning, and artificial intelligence. Additionally, it supports contributions that bridge traditional statistical approaches with modern data science techniques, fostering interdisciplinary innovation. Special emphases are provided on recent topics like statistical learning, deep learning, high-dimensional problems, semi-parametric models, biostatistics, random networks, bioinformatics etc. By publishing rigorously peer-reviewed research, the Journal aims to serve as a valuable resource for academics, researchers, and practitioners. The journal also seeks to promote the growth of the probability and statistics community in India while engaging with developments from around the world. Contributions that highlight innovative applications and theoretical advances are especially welcome, as are studies that discuss novel methodologies or applications of existing methods in unique contexts.