JIIT

Academic Experts

Dr. Ayush Tripathi

ASSISTANT PROFESSOR(GRADE II)

ayush.tripathi@jiit.ac.in

Biography

Dr. Ayush Tripathi currently serving as an Assistant Professor in the Department of Mathematics at Jaypee Institute of Information Technology, Noida. He is an academician and researcher specializing in Statistical Inference, Bayesian Statistics, and Data Science, with over four years of teaching experience. He earned his Ph.D. in Statistics from the Institute of Science, Banaras Hindu University, where his research focused on lifetime models based on record data.
Prior to joining JIIT, he has held positions at Manipal University Jaipur and Banasthali Vidyapith. His teaching covers a range of courses, including Probability and Statistics, Statistical Inference, Bayesian Statistics, Survey Sampling, and Demography, at both undergraduate and postgraduate levels.
He has published several peer-reviewed papers in reputed journals. An active contributor to the academic community, he has presented at national and international conferences. He is proficient in R, Python, SPSS, and LaTeX.
Dr. Tripathi is a member of the American Statistical Association and the Indian Society for Probability and Statistics, and serves on editorial boards and as a reviewer for several journals. His work reflects a strong commitment to bridging theory with real-world applications in statistical science.

Educational Qualifications

M. Sc. (Statistics) from Banaras Hindu University, Ph.D. (Statistics) from Banaras Hindu University

Research Highlights

Dr. Ayush Tripathi’s research lies at the intersection of statistical inference, reliability analysis, and applied data analytics, with a particular emphasis on record value theory. His work advances methodologies for analyzing lifetime distributions using partial or incomplete data. Notably, he has developed inferential procedures for several probability distributions based on record values, contributing to more efficient estimation in reliability and survival analysis.
A key focus of his research has been the estimation of the reliability measure, central to stress-strength models used in engineering and biomedical applications. His contributions include both classical and Bayesian approaches that utilize record data to improve estimation accuracy when complete datasets are not available. His work also offers critical insights into data selection and methodological design, thereby enhancing model-based decision-making processes. Beyond theoretical statistics, he has applied multivariate statistical methods to real-world problems.
Overall, his research integrates rigorous theoretical development with practical application, reflecting a commitment to enhancing statistical methods for both academic and applied domains.

Areas Of Interest

Statistical Inference
Machine Learning
Bayesian Statistics
Applied Statistics
Demography

Publications

A. Tripathi, U. Singh, and S. K. Singh,, “Inferences for the DUS-Exponential Distribution Based on Upper Record Values,” Annals of Data Science, vol. 8, no. 2, pp. 347–362, 2019. https://doi.org/10.1007/s40745-019-00231-6
A. Tripathi, U. Singh, and S. K. Singh, “Estimation of P(X < Y) for Gompertz Distribution Based on Upper Records,” International Journal of Modelling and Simulation, vol. 42, no. 3, pp. 188–197, 2021. https://doi.org/10.1080/02286203.2021.1923979
A. Tripathi, U. Singh, and S. K. Singh, “Does the Type of Records Affect the Estimate of the Parameters,” Journal of Modern Applied Statistical Methods, vol. 19, no. 1, pp. eP3068, 2022. https://doi.org/10.22237/jmasm/1608552300
A. Tripathi, U. Singh, and S. K. Singh, “Model Discrimination Based on Record Values,” Journal of Scientific Research, vol. 66, no. 1, pp. 334–343, 2022. https://doi.org/10.37398/JSR.2022.660144
P. K. Tripathi, A. Tripathi, Harshika et al., “Statistical insights on the health of Indian economy: A multivariate analysis,” Journal of Data, Information and Management, vol. 4, pp. 305–327, 2022. https://doi.org/10.1007/s42488-022-00083-5