Advancing research in Statistics, Data Science, Machine Learning, Information Theory, Estimation Theory, Time Series Analysis, Copula Modelling, and Ranking & Selection.
Explore ResearchThe Statistics and Data Science (SDS) Research Group at the Department of Mathematics, Indian Institute of Technology Indore, conducts high-quality interdisciplinary research at the interface of statistics, data science, machine learning, artificial intelligence, and computational mathematics. Our research focuses on developing rigorous statistical methodologies, advanced computational algorithms, and intelligent data-driven solutions for complex real-world problems. The group's core research areas include copula modelling, estimation theory, information theory, ranking and selection, time series analysis, machine learning, deep learning, and statistical computing. We emphasize both theoretical developments and practical applications in healthcare, finance, reliability engineering, environmental sciences, epidemiology, and industrial analytics. Through collaborative research, innovative methodologies, and the training of young researchers, SDSRG aims to advance the frontiers of statistical science and data-driven decision making while contributing impactful solutions to scientific, engineering, and societal challenges.
Dependence modelling, multivariate distributions, risk analysis, and stochastic applications.
Machine Learning, Deep Learning, Data analytics, statistical computing, visualization, and predictive modeling.
Parameter estimation, statistical inference, robust estimation, and asymptotic analysis.
Information measures, entropy, divergence, coding, and statistical applications.
Ranking methodologies, decision making, sequential selection, and optimization under uncertainty.
Statistical modeling, forecasting, stochastic processes, and temporal data analysis.
The Statistics and Data Science Research Group (SDSRG) at IIT Indore is dedicated to advancing research in Statistics, Data Science, Machine Learning, Estimation Theory, Information Theory, Copula Modelling, Ranking and Selection, and Time Series Analysis. Through interdisciplinary research and innovative statistical methodologies, we develop theoretical foundations and practical solutions for real-world data-driven problems.
Researchers
Alumni
Publications
Research Projects
Our research activities are supported by various national and international funding agencies, fostering innovation and interdisciplinary collaborations.
Research Areas: Copula Modelling, Data Science, Estimation Theory, Information Theory, Ranking and Selection, and Time Series Analysis