Anshul Singh

I recently completed an M.Sc. in Mathematics, building a strong foundation in Statistics and Pure and Applied mathematics, after earning a B.Sc. (Honours) in Mathematics from the University of Delhi. My academic interests lie at the intersection of Theoretical Statistical Modelling, Data-driven methodology development and Trustworthy AI.

I completed my Master’s thesis under the supervision of Dr. Neeraj Joshi, focusing on the development of novel statistical methodologies to address the theoretical and inferential challenges arising from incomplete and degraded data. Part I, titled Multi-Component Stress–Strength Reliability under Middle Censoring Scheme, developed novel classical and Bayesian inferential procedures for estimating multicomponent stress–strength reliability from middle-censored data. This work has been published in the Journal of Statistical Computation and Simulation and can be viewed here. Part II extends this research by developing a unified inferential framework for multicomponent stress–strength reliability under the simultaneous presence of Middle Censoring and Numerical Masking, motivated by the reliability assessment of satellite telemetry and other modern engineering systems in which observations are both incomplete and distorted. By integrating Bayesian and classical frequentist methodologies, I developed a robust framework for predicting system failure under degraded observational environments. This research is currently being extended into a full-length manuscript, which is under preparation for submission to a peer-reviewed journal.

My broader research interests include Probabilistic Machine Learning, Trustworthy AI, Statistical inference, Bayesian analysis, Missing data and Interdisciplinary statistical research. I also have a strong technical interest in statistical modelling and scientific computing, with experience in Python, R, and C/C++, as well as Machine learning for scientific data. I intend to pursue a Ph.D. in Statistics, focusing on problems that require both mathematical depth and computational innovation to develop statistically principled solutions with real-world impact.