My primary research interest lies in multiple hypothesis testing, with a strong focus on developing adaptive and robust false discovery rate (FDR) controlling procedures for large-scale inference problems. A central component of my work is the estimation of the proportion of true null hypotheses, along with its theoretical and practical implications for adaptive multiple testing methods. My doctoral research and subsequent publications address key challenges arising from discrete and non-homogeneous p-value distributions, where classical continuous-based procedures often fail. Current and future work aims to integrate multiple testing with modern variable selection and high-dimensional inference frameworks. My research interests also include statistical modelling and reliability analysis, focusing on flexible count data models, unit-interval distributions, and stress–strength and step-stress reliability frameworks. I also work on methodological and applied statistical problems arising in environmental studies, biomedical data, and social sciences. I am open to interdisciplinary collaborations that involve applied, data-driven problems.