Assoc. Prof. Dr. Kalanka Jayalath | Statistics | Best Researcher Award
University of Houston Clear-Lake | United States
Academic Background
Assoc. Prof. Dr. Kalanka Jayalath, PhD, is a distinguished academic and researcher in the Department of Mathematics and Statistics at the University of Houston–Clear Lake. He earned his Doctor of Philosophy in Statistical Science from Southern Methodist University, a Master of Science in Statistics from Sam Houston State University, and a Bachelor of Science (Honors) in Mathematics from the University of Peradeniya, Sri Lanka. His academic career is marked by excellence and scholarly recognition, supported by prestigious fellowships and faculty development awards. With 192 total citations, an h-index of 7, and an i10-index of 6 on Google Scholar, alongside 49 citations across 47 documents on Scopus, Dr. Jayalath’s research demonstrates a strong and growing impact in the statistical sciences.
Research Focus
Assoc. Prof. Dr. Kalanka Jayalath research interests encompass spatial point processes, Bayesian inference, survival analysis, analysis of variance and means, and data mining. He also actively explores sports analytics and computational statistics, contributing to both theoretical advancements and applied research across disciplines.
Work Experience
Assoc. Prof. Dr. Kalanka Jayalath currently serves as Program Chair and Associate Professor in the Department of Mathematics and Statistics at the University of Houston–Clear Lake, where he teaches and mentors both undergraduate and graduate students. His previous academic appointments include Assistant Professor at Stephen F. Austin State University. Beyond academia, he worked as a Statistical Consultant at Frito-Lay North America, providing expert guidance in experimental design and data analysis, bridging the gap between academic theory and industrial application.
Key Contributions
Assoc. Prof. Dr. Kalanka Jayalath has made significant contributions to statistical modeling through his research on spatial data analysis, robust parameter estimation, and Bayesian survival models. His innovative approaches to analyzing right-censored and right-skewed data have enhanced methodologies in applied mathematics, engineering, and computational sciences.
Awards & Recognition
Assoc. Prof. Dr. Kalanka Jayalath has been recognized with multiple distinctions, including the Center for Faculty Development Texas Research & Scholarship Award from the University of Houston–Clear Lake and the Distinguished Grant Award for Collaborative Research from Stephen F. Austin State University. His exceptional contributions to statistical science and academia have earned him the Best Researcher Award.
Professional Roles & Memberships
Assoc. Prof. Dr. Kalanka Jayalath serves as an editorial board member for the Austin Statistics Journal, Insight-Statistics Journal, and Journal of Comprehensive Pure and Applied Mathematics (JCPAM). Additionally, he is an active reviewer for leading journals such as the Journal of Statistical Computation and Simulation, Journal of Applied Statistics, Stats Journal, and Soft Computing. He is a member of the American Statistical Association, the International Statistical Engineering Association, and the Pi Mu Epsilon Mathematical Honor Society.
Publication Profile
Scopus | Orcid | Google Scholar
Featured Publications
Jayalath, K. P. (2024). Improved Bayesian inferences for right-censored Birnbaum–Saunders data. Mathematics, 12(6), 874.
Jayalath, K. P., & Ng, H. K. T. (2022). A graphical alternative for multiple group comparisons in analysis of covariance. Applied Stochastic Models in Business and Industry, 38(6), 1172–1195.
Jayalath, K. P., & Chhikara, R. S. (2022). Survival analysis for the inverse Gaussian distribution with the Gibbs sampler. Journal of Applied Statistics.
Jayalath, K. P. (2021). Fiducial inference on the right-censored Birnbaum–Saunders data via Gibbs sampler. Stats, 4(2), 385–399.
Jayalath, K. P., & Ng, H. K. T. (2020). Analysis of means approach in advanced designs. Applied Stochastic Models in Business and Industry, 36(3), 501–520.
Impact Statement / Vision
Assoc. Prof. Dr. Kalanka Jayalath envisions promoting the integration of advanced statistical modeling, Bayesian inference, and computational analytics to address complex real-world challenges. Through his research and teaching, he aims to inspire data-driven innovation and enhance interdisciplinary collaboration in statistical education and applied research, shaping future generations of statisticians and data scientists.