Anshul Singh

I am a Master’s student in Mathematics with strong foundation in Mathematics (Pure and Applied) & Statistics, having completed a Bachelor of Science (Honours) in Mathematics. My academic interests lie at the intersection of theoretical statistical modeling and data-driven methodology development, with a particular focus on problems where classical assumptions fail due to censoring, missingness and structural dependence arising in real word problems and applied sciences.

In my completed Master’s thesis, titled “Multi-Component Stress–Strength Reliability under Middle Censoring,” I developed novel statistical methodologies to rigorously address the theoretical and inferential complexities arising from middle-censored data, under the supervision of Dr. Neeraj Joshi. By orchestrating a synergy between Bayesian methods and Classical frequentist approach, I developed a more robust approach for predicting system failure. This research has been crystallized into a manuscript, which is currently under review for publication. I am currently undertaking an extended Master’s level research project, approved under the Institute’s academic framework, which builds upon my initial thesis work to develop a more comprehensive and unified structure for addressing complex data problems into a unified and extensible framework for inference.

My broader research areas include Statistical (Parametric and Non-Parametric) Inference, Bayesian Analysis, Reliability/Survival Analysis, Topological Data Analysis, Probability Theory and Interdisciplinary Statistical Research, along with a technical focus on Statistical Modeling/Computing, programming lanuages such as Python, R, C/C++ and Machine Learning for Scientific Data. I intend to pursue a PhD in Statistics, focusing on problems that demand both mathematical depth and computational innovation, that lead to statistically principled solutions with real-world impact.